OR

Browse By

Professor Brian Lovell

Professor

School of Electrical Engineering and Computer Science
Faculty of Engineering, Architecture and Information Technology
lovell@eecs.uq.edu.au
+61 7 336 54134

Overview

Brian C. Lovell, born in Brisbane, Australia in 1960, received his BE in Electrical Engineering (Honours I) in 1982, BSc in Computer Science in 1983, and PhD in Signal Processing in 1991, all from the University of Queensland (UQ). Currently, he is the Project Leader of the Advanced Surveillance Group at UQ. Professor Lovell served as the President of the International Association of Pattern Recognition from 2008 to 2010, is a Senior Member of the IEEE, a Fellow of the IEAust, Fellow of the Asia-Pacific AI Association, and has been a voting member for Australia on the Governing Board of the International Association for Pattern Recognition since 1998.

He is an Honorary Professor at IIT Guwahati, India; an Associate Editor of the Pattern Recognition Journal; an Associate Editor-in-Chief of the Machine Learning Research Journal; a member of the IAPR TC4 on Biometrics; and a member of the Awards Committee and Education Committee of the IEEE Biometrics Council.

In addition, Professor Lovell has chaired and co-chaired numerous international conferences in the field of pattern recognition, including ICPR2008, ACPR2011, ICIP2013, ICPR2016, and ICPR2020. His Advanced Surveillance Group has collaborated with port, rail, and airport organizations, as well as several national and international agencies, to develop technology-based solutions for operational and security concerns.

His current research projects are in the fields of:

  • Artificial Intelligence
  • StyleGAN
  • Stable Diffusion
  • Deep Learning
  • Biometrics
  • Robust Face Recognition using Deep Learning
  • Masked Face Recognition for COVID-19 Pandemic
  • Adversarial Attacks on AI Systems
  • Digital Pathology
  • Neurofibroma Detection and Assessment
  • Object Detection with Deep Learning

I am actively recruiting PhD students in Artificial Intelligence to work with my team. If you are interested and have a strong record from a good university, with a publication in a good conference such as CVPR, ICCV, ECCV, or MICCAI please send your CV to me. Full Scholarships (Tuition and Living) can be awarded within one month for truly exceptional candidates.

Research Interests

  • Face Recognition with Deep Learning
    We develop new technologies to improve face recognition. Our group is first in the world to develop face recognition databases based entirely on synthetic faces. Other aspects of face recognition and affective computing (determining emotions from facial expressions) are current research themes within the group.
  • Object Detection Using Deep Learning
    We are researching improved techniques to identify small objects with high precision
  • Synthetic Face and Image Generation
    We were the first to investigate training face recognition systems on synthetic faces.

Research Impacts

I have been pleased that my biometrics and other research has and is being been adopted commercially worldwide. My earlier face recognition systems have been installed by the University of San Francisco and Swinburne University among many other sites. More recently we have developed face recognition systems that are insensitive to the wearing of masks. These systems depend on our EDITH Ethical Face database of synthetic faces. To the best of our knowledge, we are the only group worldwide who can synthesise faces to order to train advanced ethical face recognition systems.

These systems have been adopted in the UK in 2020 by Facewatch Ltd and are currently being considered by the UK National Health Service and also Queensland Health to manage COVID 19 quarantine facilities and border control. In 2020-2021 we developed a touchless face mask fitting system for health workers to reduce the wastage of PPE and improve COVID19 management. This system is deployed on Queensland Health IT infrastructure in February 2021 and is planned to be made available nationally and internationally. The system has the potential to save millions of dollars in wasted PPE.

PRIZES, HONOURS AND AWARDS

Fellow of the IAPR, 2008 Multiple Best Paper prizes. Awarded Certificate of Recognition as most downloaded author at UQ by UQCybrary. Over 26,000 copies of my research papers were downloaded from the UQ EPrints archive in the 12 months ending May, 2005. APICTA Trophy for Best Research and Development, 2011, Face Recognition in a Crowd IFSEC Trophy 2011, Best CCTV Product of the Year (excluding cameras and lens), Face Recognition in a Crowd Technology Winner, ADS Security Innovation Award, 2021, Galahad facial detection and recognition software, awarded by the UK Home Office at the Security and Policing Show on March 9, 2021.

Qualifications

  • Doctor of Philosophy, The University of Queensland
  • Bachelor of Science, The University of Queensland
  • Bachelor (Honours) of Engineering, The University of Queensland

Publications

View all Publications

Grants

View all Grants

Supervision

View all Supervision

Available Projects

  • Detecting and Classifying Neurofibromas using Deep Learning

    Neurofibromatosis type 1 (NF1) is one of the most common single-gene inherited disorders globally, with an incidence of 1/2500 individuals. While several phenotypes are associated with the disorder, the most common manifestation is cutaneous neurofibroma. The majority of adults develop these distressing cutaneous tumours (cNF), which increase in severity with age. Adult patients report cosmetic disfigurement due to cNF as the greatest burden of living with NF1. There is no way to predict tumour severity which can range from <100 to thousands. Youth and families experience reduced quality of life due to concerns about this uncertain future. We don’t yet understand why this condition is so variable or have any effective medical treatments. In the proposed research, we will assemble a consortium of internationally recognised experts in NF1 with access and capacity to recruit and phenotype patients to drive the largest genome-wide association and epigenome-wide association studies of the modifier gene networks driving the cutaneous phenotypic variance in NF1. We will then use individualised pharmacological annotation of these networks to identify precision treatment options to mitigate the most distressing and life quality damaging aspects of this devastating illness.

  • Classifying Gram Stain Images Using Transformers and Deep Learning

    Microscopic diagnosis of Gram stain smears is one of the most time and labor intensive tasks in the clinical setting. With the recent development of automated digital pathology scanners, it is now possible to economically obtain high-resolution Gram stain whole slide images for routine diagnosis. This finally opens the doorway to automated identification of bacteria types from digital images in a clinical setting. However, Gram stain whole slide images comprise billions of pixels and suffer from high morphological heterogeneity as well as from many different types of artifacts. Identifying multiple types of tiny bacteria with various densities from an extremely large whole slide image is incredibly challenging. To this end, we propose an end-to-end framework with a novel loss function that tackles the patch aggregation while considering the correlation of different labels in this multi-label scenario. Our framework first effectively integrates the relations among multiple patch features, and then utilizes a class aggregator to generate a robust slide-level feature representation under multi-label setting. Furthermore, we propose a novel loss function integrating two regularization terms: 1) a negative regulator that reduces the confusion between bacteria and negative samples without any bacteria, and 2) an adversarial loss that alleviates the impact of background specification among various tissue samples. We show that the proposed method achieves superior performance compared to several state-of-the-art methods.

  • Incremental Learning for AI

    Incremental learning requires a model to continually learn new tasks from streaming data. However, traditional fine-tuning of a well-trained deep neural network on a new task will dramatically degrade performance on the old task — a problem known as catastrophic forgetting. We address this issue in the context of anchor-free object detection, which is a new trend in computer vision as it is simple, fast, and flexible. Simply adapting current incremental learning strategies fails on these anchor-free detectors due to lack of consideration of their specific model structures. To deal with the challenges of incremental learning on anchor-free object detectors, we propose a novel incremental learning paradigm called Selective and Inter-related Distillation (SID). In addition, a novel evaluation metric is proposed to better assess the performance of detectors under incremental learning conditions. By selective distilling at the proper locations and further transferring additional instance relation knowledge, our method demonstrates significant advantages on the benchmark datasets PASCAL VOC and COCO.

View all Available Projects

Publications

Book

Book Chapter

  • Dynamic Curriculum Learning via In-Domain Uncertainty for Medical Image Classification

    Li, Chaoyi, Li, Meng, Peng, Can and Lovell, Brian C. (2023). Dynamic Curriculum Learning via In-Domain Uncertainty for Medical Image Classification. Lecture Notes in Computer Science. (pp. 747-757) Cham: Springer Nature Switzerland. doi: 10.1007/978-3-031-43904-9_72

  • Deep learning in person re-identification for cyber-physical surveillance systems

    Wu, Lin, Lovell, Brian C. and Wang, Yang (2019). Deep learning in person re-identification for cyber-physical surveillance systems. Deep Learning Applications for Cyber Security. (pp. 45-72) edited by Alazab, M. and Tang, M. J.. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-13057-2_3

  • Solving classification problems on human epithelial type 2 cells for anti-nuclear antibodies test: traditional versus contemporary approaches

    Wiliem, Arnold and Lovell, Brian C. (2017). Solving classification problems on human epithelial type 2 cells for anti-nuclear antibodies test: traditional versus contemporary approaches. Pattern recognition and Big Data. (pp. 605-632) edited by Amita Pal and Sankar K. Pal. Singapore: World Scientific. doi: 10.1142/9789813144552_0018

  • Coordinate coding on the riemannian manifold of symmetric positive-definite matrices for image classification

    Harandi, Mehrtash, Basirat, Mina and Lovell, Brian C. (2015). Coordinate coding on the riemannian manifold of symmetric positive-definite matrices for image classification. Riemannian computing in computer vision. (pp. 345-361) edited by Pavan Turaga and Anuj Srivastava. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-22957-7_16

  • Secure face recognition for mobile applications

    Lovell, Brian C. and Smith, Daniel F. (2014). Secure face recognition for mobile applications. Face Recognition in Adverse Conditions. (pp. 359-386) edited by Maria de Marsico, Michele Nappi and Massimo Tistarelli. Hershey, United States: Hershey Information Science Reference. doi: 10.4018/978-1-4666-5966-7.ch017

  • Graph-embedding discriminant analysis on Riemannian manifolds for visual recognition

    Shirazi, Sareh, Alavi, Azadeh, Harandi, Mehrtash T. and Lovell, Brian C. (2013). Graph-embedding discriminant analysis on Riemannian manifolds for visual recognition. Graph Embedding for Pattern Analysis. (pp. 157-176) edited by Yun Fu and Yunqian Ma. New York, NY, USA: Springer. doi: 10.1007/978-1-4614-4457-2

  • Machine learning applications in computer vision

    Harandi, Mehrtash, Taheri, Javid and Lovell, Brian C. (2013). Machine learning applications in computer vision. Image processing: Concepts, methodologies, tools, and applications. (pp. 896-926) edited by Mehdi Khosrow-Pour. Hershey, PA., United States: IGI Global. doi: 10.4018/978-1-4666-3994-2.ch045

  • Motion estimation in colour image sequences

    Benois-Pineau, Jenny, Lovell, Brian C. and Andrews, Robert J. (2013). Motion estimation in colour image sequences. Advanced color image processing and analysis. (pp. 377-395) edited by Christine Fernandez-Maloigne. New York, NY, United States: Springer. doi: 10.1007/978-1-4419-6190-7_11

  • Machine learning applications in computer vision

    Harandi, Mehrtash, Taheri, Javid and Lovell, Brian C. (2012). Machine learning applications in computer vision. Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques. (pp. 99-132) Hershey, Pennsylvania, USA: IGI Global. doi: 10.4018/978-1-4666-1833-6.ch007

  • Intelligent surveillance and pose-invariant 2D face classification

    Lovell, Brian C., Sanderson, Conrad and Shan, Ting (2010). Intelligent surveillance and pose-invariant 2D face classification. Machine interpretation of patterns: Image analysis and data mining. (pp. 207-229) edited by Rajat K. de, Deba Prasad Mandel and Ashish Ghosh. Singapore, Republic of Singapore: World Scientific. doi: 10.1142/9789814299190_0011

  • Intelligent CCTV for mass transport security: Challenges and opportunities for video and face processing

    Sanderson, Conrad, Bigdeli, Abbas, Shan, Ting, Chen, Shaokang, Berglund, Erik and Lovell, Brian C. (2009). Intelligent CCTV for mass transport security: Challenges and opportunities for video and face processing. Progress in Computer Vision and Image Analysis. (pp. 557-573) edited by Horst Bunke, Juan José Villanueva, Gemma Sánchez and Xavier Otazu. Singapore: World Scientific. doi: 10.1142/9789812834461_0030

  • An Automatic Offline Signature and Forgery Detection System

    Madasu, Vamsi Krishna and Lovell, Brian C. (2008). An Automatic Offline Signature and Forgery Detection System. Pattern recognition technologies and applications : Recent advances. (pp. 63-89) edited by Brijesh Verma and Michael Blumenstein. Hersey, PA: Information Science Reference. doi: 10.4018/978-1-59904-807-9.ch004

  • Real-time face detection and classification for ICCTV

    Lovell, Brian C., Chen, Shaokang and Shan, Ting (2008). Real-time face detection and classification for ICCTV. Encyclopedia of Data Warehousing and Mining. (pp. 1-19) edited by John Wang. Hershey, PA: Information Science Reference.

  • Robust Face recognition for Data Mining

    Lovell, Brian C. and Chen, Shaokang (2008). Robust Face recognition for Data Mining. Data Warehousing and Mining: Concepts, Methodologies, Tools and Applications. (pp. 3621-3629) edited by John Wang. Hershey, PA: Information Science Reference.

  • Robust face recognition technique for a real-time embedded face recognition system

    Shan, Ting, Bigdeli, Abbas, Lovell, Brian C. and Chen, Shaokang (2008). Robust face recognition technique for a real-time embedded face recognition system. Pattern recognition technologies and applications : Recent advances. (pp. 188-211) edited by Brijesh Verma and Michael Blumenstein. Hershey, PA, U.S.A.: Information Science Reference. doi: 10.4018/978-1-59904-807-9.ch008

  • Support vector machines for business applications

    Lovell, Brian C. and Walder, Christian J. (2007). Support vector machines for business applications. Mathematical Methods for Knowledge Discovery and Data Mining. (pp. 82-100) IGI Global. doi: 10.4018/978-1-59904-528-3.ch005

  • Intelligent CCTV via planetary sensor network

    Shan, T., Lovell, B.C and Chen, S. (2007). Intelligent CCTV via planetary sensor network. Sensor Networks and Configuration : Fundamentals, Standards, Platforms, and Applications. (pp. 463-484) edited by Nitaigour P. Mahalik. Berlin, Germany: Springer.

  • Support Vector Machines for Business Applications

    Lovell, Brian C. and Walder, Christian J. (2006). Support Vector Machines for Business Applications. Business Applications and Computational Intelligence. (pp. 267-290) edited by K. Voges and N. Pope. Hershey, PA., U.S.A.: Idea Group. doi: 10.4018/978-1-59140-702-7.ch014

  • Hidden Markov models for spatio-temporal pattern recognition

    Lovell, Brian C. and Caelli, Terry (2005). Hidden Markov models for spatio-temporal pattern recognition. Handbook of pattern recognition and computer vision. (pp. 25-40) edited by C. H. Chen and P. S. P. Wang. Singapore: World Scientific Publications. doi: 10.1142/9789812775320_0002

  • Robust Face Recognition for Data Mining

    Lovell, Brian C. and Chen, Shaokang (2005). Robust Face Recognition for Data Mining. Encyclopedia of Data Warehousing and Mining. (pp. 965-972) edited by Wang, John. Hershey, USA: Idea Group.

Journal Article

Conference Publication

  • Knowing the unknown: open-set bacteria classification in gram stain microscopic images

    Alhammad, Sarah and Lovell, Brian C. (2023). Knowing the unknown: open-set bacteria classification in gram stain microscopic images. 2023 International Joint Conference on Neural Networks (IJCNN), Gold Coast, QLD, Australia, 18-23 June 2023. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ijcnn54540.2023.10191073

  • Efficient cell labelling for gram stain WSIs

    Alhammad, Sarah, Zhang, Teng, Zhao, Kun, Hobson, Peter, Jennings, Anthony and Lovell, Brian C. (2022). Efficient cell labelling for gram stain WSIs. 26th International Conference on Pattern Recognition (ICPR), Montreal, QC, Canada, 21-25 August 2022. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/icpr56361.2022.9956490

  • MedViTGAN: end-to-end conditional GAN for histopathology image augmentation with vision transformers

    Li, Meng, Li, Chaoyi, Hobson, Peter, Jennings, Tony and Lovell, Brian C. (2022). MedViTGAN: end-to-end conditional GAN for histopathology image augmentation with vision transformers. 26th International Conference on Pattern Recognition / 8th International Workshop on Image Mining - Theory and Applications (IMTA), Montreal, Canada, 21-25 August 2022. New York, NY USA: Institute of Electrical and Electronics Engineers. doi: 10.1109/icpr56361.2022.9956431

  • Few-Shot Class-Incremental Learning from an Open-Set Perspective

    Peng, Can, Zhao, Kun, Wang, Tianren, Li, Meng and Lovell, Brian C. (2022). Few-Shot Class-Incremental Learning from an Open-Set Perspective. 17th European Conference on Computer Vision - ECCV 2022, Tel Aviv, Israel, 23–27 October 2022. Cham, Switzlerland: Springer Nature. doi: 10.1007/978-3-031-19806-9_22

  • Efficient DNN-Based Classification of Whole Slide Gram Stain Images for Microbiology

    Alhammad, Sarah, Zhao, Kun, Jennings, Anthony, Hobson, Peter, Smith, Daniel F., Baker, Brett, Staweno, Justin and Lovell, Brian C. (2021). Efficient DNN-Based Classification of Whole Slide Gram Stain Images for Microbiology. 2021 Digital Image Computing: Techniques and Applications (DICTA), Gold Coast, QLD Australia, 29 November - 1 December 2021. Piscataway, NJ United States: IEEE. doi: 10.1109/dicta52665.2021.9647415

  • Minimizing labeling cost for nuclei instance segmentation and classification with cross-domain images and weak labels

    Yang, Siqi, Zhang, Jun, Huang, Junzhou, Lovell, Brian C. and Han, Xiao (2021). Minimizing labeling cost for nuclei instance segmentation and classification with cross-domain images and weak labels. 35th AAAI Conference on Artificial Intelligence, AAAI 2021, Virtual, 2-9 February 2021. Menlo Park, CA, United States: Association for the Advancement of Artificial Intelligence. doi: 10.1609/aaai.v35i1.16150

  • Boundary guided image translation for pose estimation from ultra-low resolution thermal sensor

    Kurihara, Kohei, Wang, Tianren, Zhang, Teng and Lovell, Brian C. (2021). Boundary guided image translation for pose estimation from ultra-low resolution thermal sensor. 25th International Conference on Pattern Recognition (ICPR), Online, 10-15 January 2021. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICPR48806.2021.9412250

  • Deep adaptive few example learning for microscopy image cell counting

    Li, Meng, Zhao, Kun, Peng, Can, Hobson, Peter, Jennings, Tony and Lovell, Brian C. (2021). Deep adaptive few example learning for microscopy image cell counting. 2021 Digital Image Computing: Techniques and Applications (DICTA), Gold Coast, QLD, Australia, 29 November - 1 December 2021. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers . doi: 10.1109/DICTA52665.2021.9647246

  • Faces à la carte: Text-to-face generation via attribute disentanglement

    Wang, Tianren, Zhang, Teng and Lovell, Brian (2021). Faces à la carte: Text-to-face generation via attribute disentanglement. 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, United States, 3-8 January 2021. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/WACV48630.2021.00342

  • Unsupervised domain adaptive object detection using forward-backward cyclic adaptation

    Yang, Siqi, Wu, Lin, Wiliem, Arnold and Lovell, Brian C. (2021). Unsupervised domain adaptive object detection using forward-backward cyclic adaptation. 15th Asian Conference on Computer Vision, Kyoto, Japan, 30 November-4 December 2020. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-69535-4_8

  • SOS: Selective Objective Switch for Rapid Immunofluorescence Whole Slide Image Classification

    Maksoud, Sam, Zhao, Kun, Hobson, Peter, Jennings, Anthony and Lovell, Brian C. (2020). SOS: Selective Objective Switch for Rapid Immunofluorescence Whole Slide Image Classification. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA United States, 13-19 June 2020. Piscataway, NJ United States: IEEE. doi: 10.1109/cvpr42600.2020.00392

  • CORAL8: Concurrent object regression for area localization in medical image&nbsp;panels

    Maksoud, Sam, Wiliem, Arnold, Zhao, Kun, Zhang, Teng, Wu, Lin and Lovell, Brian (2019). CORAL8: Concurrent object regression for area localization in medical image panels. MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention, Shenzhen, China, 13-17 October 2019. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-32239-7_48

  • Cannygan: Edge-Preserving Image Translation with Disentangled Features

    Wang, Tianren, Zhang, Teng, Liu, Liangchen, Wiliem, Arnold and Lovell, Brian (2019). Cannygan: Edge-Preserving Image Translation with Disentangled Features. 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, 22-25 September 2019. Piscataway, NJ United States: IEEE Computer Society. doi: 10.1109/ICIP.2019.8803828

  • DGDI: a dataset for detecting glomeruli on renal direct immunofluorescence

    Zhao, Kun, Tang, Yu Ji Jerry, Zhang, Teng, Carvajal, Johanna, Smith, Daniel F., Wiliem, Arnold, Hobson, Peter, Jennings, Anthony and Lovell, Brian C. (2019). DGDI: a dataset for detecting glomeruli on renal direct immunofluorescence. 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018, Canberra, ACT, Australia, 10-13 December 2018. New York, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/DICTA.2018.8615769

  • Deep corrosion assessment for electrical transmission towers

    Zhang, Teng, Liu, Liangchen, Wiliem, Arnold, Connor, Stephen, Ilich, Zelkjo, Van Der Draai, Eddie and Lovell, Brian (2019). Deep corrosion assessment for electrical transmission towers. 2019 Digital Image Computing: Techniques and Applications, DICTA 2019, Perth, WA, Australia, 2-4 December 2019. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/DICTA47822.2019.8945905

  • Deep inspection: an electrical distribution pole parts study VIA deep neural networks

    Liu, Liangchen, Zhang, Teng, Zhao, Kun, Wiliem, Arnold, Astin-Walmsley, Kieren and Lovell, Brian (2019). Deep inspection: an electrical distribution pole parts study VIA deep neural networks. IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, 22-25 September 2019. Piscataway, NJ, United States: IEEE Computer Society. doi: 10.1109/ICIP.2019.8803415

  • Deep instance-level hard negative mining model for histopathology images

    Li, Meng, Wu, Lin, Wiliem, Arnold, Zhao, Kun, Zhang, Teng and Lovell, Brian (2019). Deep instance-level hard negative mining model for histopathology images. MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention, Shenzhen, China, 13-17 October 2019. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-32239-7_57

  • Deep-learning from mistakes: automating cloud class refinement for sky image segmentation

    Dianne, Gemma, Wiliem, Arnold and Lovell, Brian C. (2019). Deep-learning from mistakes: automating cloud class refinement for sky image segmentation. 2019 Digital Image Computing: Techniques and Applications, DICTA 2019, Perth, WA, Australia, 2-4 December 2019. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/DICTA47822.2019.8946028

  • Early experience of depth estimation on intricate objects using generative adversarial networks

    San, Wai Y. K., Zhang, Teng, Chen, Shaokang, Wiliem, Arnold, Stefanelli, Dario and Lovell, Brian C. (2019). Early experience of depth estimation on intricate objects using generative adversarial networks. 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018, Canberra, ACT, Australia, 10-13 December 2018. New York, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/DICTA.2018.8615783

  • Recurrent attention networks for medical concept prediction

    Maksoud, Sam, Wiliem, Arnold and Lovell, Brian (2019). Recurrent attention networks for medical concept prediction. 20th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF 2019, Lugano, Switzerland, 9 - 12 September 2019. Aachen, Germany: CEUR-WS.

  • To what extent does downsampling, compression, and data scarcity impact renal image analysis?

    Peng, Can, Zhao, Kun, Wiliem, Arnold, Zhang, Teng, Hobson, Peter, Jennings, Anthony and Lovell, Brian C. (2019). To what extent does downsampling, compression, and data scarcity impact renal image analysis?. 2019 Digital Image Computing: Techniques and Applications (DICTA), Perth, WA Australia, 2-4 December 2019. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/DICTA47822.2019.8945813

  • Training region selector for gram stained slides with limited data: a data distillation approach

    Zhang, Teng, Wiliem, Arnold, Hobson, Peter, Jennings, Anthony and Lovell, Brian C. (2019). Training region selector for gram stained slides with limited data: a data distillation approach. 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018, Canberra, ACT, 10-13 December 2018. New York, NY, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/DICTA.2018.8615760

  • An innovative approach to estimate carbon status for improved crop load management in apple

    Darbyshire, R., San, W. Y. K., Plozza, T., Lovell, B. C., Flachowsky, H., Wünsche, J. and Stefanelli, D. (2018). An innovative approach to estimate carbon status for improved crop load management in apple. International Symposium on Flowering, Fruit Set and Alternate Bearing, Palermo, Italy, 19-23 June 2017. Leuven, Belgium: International Society for Horticultural Science. doi: 10.17660/ActaHortic.2018.1229.43

  • It takes two to tango: cascading off-the-shelf face detectors

    Yang, Siqi, Wiliem, Arnold and Lovell, Brian C. (2018). It takes two to tango: cascading off-the-shelf face detectors. IEEE Conference on Computer Vision and Pattern Recognition Workshops in Biometrics (CVPRW), Salt Lake City, UT, United States, 18 - 22 June 2018. Piscataway, NJ, United States: IEEE Computer Society. doi: 10.1109/CVPRW.2018.00095

  • SlideNet: fast and accurate slide quality assessment based on deep neural networks

    Zhang, Teng, Carvajal, Johanna, Smith, Daniel F., Zhao, Kun, Wiliem, Arnold, Hobson, Peter, Jennings, Anthony and Lovell, Brian C. (2018). SlideNet: fast and accurate slide quality assessment based on deep neural networks. 24th International Conference on Pattern Recognition, ICPR 2018, Beijing, China, 20 - 24 August 2018. Washington, DC, United States: IEEE Computer Society. doi: 10.1109/ICPR.2018.8546205

  • TV-GAN: generative adversarial network based thermal to visible face recognition

    Zhang, Teng, Wiliem, Arnold, Yang, Siqi and Lovell, Brian C. (2018). TV-GAN: generative adversarial network based thermal to visible face recognition. International Conference on Biometrics (ICB), Gold Coast, QLD, Australia, 20 - 23 February 2018. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICB2018.2018.00035

  • Using LIP to gloss over faces in single-stage face detection networks

    Yang, Siqi, Wiliem, Arnold, Chen, Shaokang and Lovell, Brian C. (2018). Using LIP to gloss over faces in single-stage face detection networks. 15th European Conference on Computer Vision, ECCV 2018, Munich, Germany, 8-14 September 2018. Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-01267-0_39

  • An early experience toward developing computer aided diagnosis for gram-stained smears images

    Carvajal, Johanna, Smith, Daniel F., Zhao, Kun, Wiliem, Arnold, Finucane, Paul, Hobson, Peter, Jennings, Anthony, McDougall, Rodney and Lovell, Brian (2017). An early experience toward developing computer aided diagnosis for gram-stained smears images. 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, United States, 21-26 July 2017. Piscataway, NJ, United States: IEEE. doi: 10.1109/CVPRW.2017.113

  • How do you develop a face detector for the unconstrained environment?

    San, Wai Y. K., Chen, Shaokang, Wiliem, Arnold, Di, Binn and Lovell, Brian C. (2017). How do you develop a face detector for the unconstrained environment?. 2016 International Conference on Image and Vision Computing New Zealand, IVCNZ 2016, Palmerston North, New Zealand, 21 - 22 November 2016. Piscataway, NJ, United States: IEEE Computer Society. doi: 10.1109/IVCNZ.2016.7804414

  • Landmark manifold: revisiting the Riemannian manifold approach for facial emotion recognition

    Zhao, Kun, Yang, Siqi, Wiliem, Arnold and Lovell, Brian C. (2017). Landmark manifold: revisiting the Riemannian manifold approach for facial emotion recognition. 2016 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico, 4-8 December 2016. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers . doi: 10.1109/ICPR.2016.7899782

  • The GIST of aligning faces

    Yang, Siqi, Wiliem, Arnold and Lovell, Brian C. (2017). The GIST of aligning faces. 2016 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico, 4-8 December 2016. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICPR.2016.7900095

  • To face or not to face: towards reducing false positive of face detection

    Yang, Siqi, Wiliem, Arnold and Lovell, Brian C. (2017). To face or not to face: towards reducing false positive of face detection. 2016 International Conference on Image and Vision Computing New Zealand, IVCNZ 2016, Palmerston North, New Zealand, 21 - 22 November 2016. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/IVCNZ.2016.7804415

  • Towards Miss Universe automatic prediction: the evening gown competition

    Carvajal, Johanna, Wiliem, Arnold, Sanderson, Conrad and Lovell, Brian (2017). Towards Miss Universe automatic prediction: the evening gown competition. 23rd International Conference on Pattern Recognition, ICPR 2016, Cancun, Mexico, 4 - 8 December 2016. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICPR.2016.7899781

  • Unsupervised automatic attribute discovery method via multi-graph clustering

    Liu, Liangchen, Nie, Feiping, Zhang, Teng, Wiliem, Arnold and Lovell, Brian C. (2017). Unsupervised automatic attribute discovery method via multi-graph clustering. International Conference on Pattern Recognition (ICPR), Cancún, México, 4-8 December 2016. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICPR.2016.7899883

  • A Comparative Evaluation of Action Recognition Methods via Riemannian Manifolds, Fisher Vectors and GMMs: Ideal and Challenging Conditions

    Carvajal, Johanna, Wiliem, Arnold, McCool, Chris, Lovell, Brian and Sanderson, Conrad (2016). A Comparative Evaluation of Action Recognition Methods via Riemannian Manifolds, Fisher Vectors and GMMs: Ideal and Challenging Conditions. Pacific-Asia Conference on Knowledge Discovery and Data Mining, Auckland, New Zealand, 19-22 April 2016. CHAM: Springer. doi: 10.1007/978-3-319-42996-0_8

  • Automatic and quantitative evaluation of attribute discovery methods

    Liu, Liangchen, Wiliem, Arnold, Chen, Shaokang and Lovell, Brian C. (2016). Automatic and quantitative evaluation of attribute discovery methods. IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, United States, 7-10 March 2016. Piscataway, NJ, United States: IEEE. doi: 10.1109/WACV.2016.7477693

  • Determining the best attributes for surveillance video keywords generation

    Liu, Liangchen, Wiliem, Arnold, Chen, Shaokang, Zhao, Kun and Lovell, Brian C. (2016). Determining the best attributes for surveillance video keywords generation. 2nd IEEE International Conference on Identity, Security and Behavior Analysis, ISBA 2016, Sendai, Japan, 29 February - 2 March 2016. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ISBA.2016.7477239

  • International Contest on Pattern Recognition techniques for indirect immunofluorescence images analysis

    Lovell, Brian C., Percannella, Gennaro, Saggese, Alessia, Vento, Mario and Wiliem, Arnold (2016). International Contest on Pattern Recognition techniques for indirect immunofluorescence images analysis. 23rd International Conference on Pattern Recognition, ICPR 2016, Cancun, Mexico, December 4, 2016-December 8, 2016. Washington, DC United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICPR.2016.7899610

  • Is Alice chasing or being chased?: Determining subject and object of activities in videos

    Zhang, Teng, Liu, Liangchen, Wiliem, Arnold and Lovell, Brian (2016). Is Alice chasing or being chased?: Determining subject and object of activities in videos. IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, United States, 7-10 March 2016. Piscataway, NJ, United States: IEEE. doi: 10.1109/WACV.2016.7477710

  • Joint recognition and segmentation of actions via probabilistic integration of spatio-temporal fisher vectors

    Carvajal, Johanna, McCool, Chris, Lovell, Brian and Sanderson, Conrad (2016). Joint recognition and segmentation of actions via probabilistic integration of spatio-temporal fisher vectors. 20th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2016 and Workshop on Biologically Inspired Data Mining Techniques, BDM 2016, Workshop on Machine Learning for Sensory Data Analysis, MLSDA 2016, Workshop on Predictive Analytics for Critical Care, PACC 2016 and Workshop on Data Mining in Business and Finance, WDMBF 2016, Auckland,, April 19, 2016-April 19, 2016. Heidelberg, Germany: Springer Verlag. doi: 10.1007/978-3-319-42996-0_10

  • Manifold convex hull (MACH): satisfying a need for SPD

    Zhao, Kun, Wiliem, Arnold, Chen, Shaokang and Lovell, Brian C. (2016). Manifold convex hull (MACH): satisfying a need for SPD. 23rd IEEE International Conference on Image Processing, ICIP 2016, Phoenix, AZ, United States, 25-28 September 2016. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICIP.2016.7532357

  • A benchmarking platform for mitotic cell classification of ANA IIF HEp-2 images

    Miros, Anastasia, Wiliem, Arnold, Holohan, Kim, Ball, Lauren, Hobson, Peter and Lovell, Brian C. (2015). A benchmarking platform for mitotic cell classification of ANA IIF HEp-2 images. International Conference on Digital Image Computing: Techniques and Applications, DICTA, Adelaide, SA, Australia, 23-25 November 2015. Piscataway, NJ, United States: IEEE. doi: 10.1109/DICTA.2015.7371213

  • A multiple covariance approach for cell detection of Gram-stained smears images

    Crossman, Matthew, Wiliem, Arnold, Finucane, Paul, Jennings, Anthony and Lovell, Brian C. (2015). A multiple covariance approach for cell detection of Gram-stained smears images. International Conference on Acoustics, Speech, and Signal Processing (ICASSP) Conference, Brisbane, QLD, Australia, 19-24 April 2015. Piscataway, NJ, United States: IEEE (Institute for Electrical and Electronic Engineers). doi: 10.1109/ICASSP.2015.7178106

  • An optimization approach to scanning skin direct immunofluorescence specimens

    Samak, Asser, Wiliem, Arnold, Hobson, Peter, Walsh, Michael, Ditchmen, Ted, Troskie, Arne, Barksdale, Sarah, Edwards, Rhonda, Jennings, Anthony and Lovell, Brian C. (2015). An optimization approach to scanning skin direct immunofluorescence specimens. International Conference on Digital Image Computing: Techniques and Applications, DICTA, Adelaide, SA, Australia, 23-25 November 2015. Piscataway, NJ, United States: IEEE. doi: 10.1109/DICTA.2015.7371230

  • Binary watermarks: a practical method to address face recognition replay attacks on consumer mobile devices

    Smith, Danny F., Wiliem, Arnold and Lovell, Brian C. (2015). Binary watermarks: a practical method to address face recognition replay attacks on consumer mobile devices. IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), Hong Kong Special Administrative Region of the People's Republic of China, 23-25 March 2015. Piscataway, NJ, United States: IEEE. doi: 10.1109/ISBA.2015.7126344

  • Detecting kangaroos in the wild: the first step towards automated animal surveillance

    Zhang, Teng, Wiliem, Arnold, Hemson, Graham and Lovell, Brian C. (2015). Detecting kangaroos in the wild: the first step towards automated animal surveillance. International Conference on Acoustics, Speech, and Signal Processing (ICASSP) Conference, Brisbane, QLD, Australia, 19-24 April 2015. Piscataway, NJ, United States: IEEE (Institute for Electrical and Electronic Engineers). doi: 10.1109/ICASSP.2015.7178313

  • Kernelised orthonormal random projection on grassmann manifolds with applications to action and gait-based gender recognition

    Zhao, Kun, Wiliem, Arnold and Lovell, Brian (2015). Kernelised orthonormal random projection on grassmann manifolds with applications to action and gait-based gender recognition. 2015 IEEE International Conference on Identity, Security and Behavior Analysis, ISBA 2015, Hong Kong, March 23-25, 2015. Piscataway, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ISBA.2015.7126348

  • Message from general and technical co-chairs

    Lovell, Brian C. and Vento, Mario (2014). Message from general and technical co-chairs. 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images, Stockholm, Sweden, 24 August 2014. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/I3A.2014.4

  • Automatic image attribute selection for zero-shot learning of object categories

    Liu, Liangchen, Wiliem, Arnold, Chen, Shaokang and Lovell, Brian C. (2014). Automatic image attribute selection for zero-shot learning of object categories. 22nd International Conference on Pattern Recognition, ICPR 2014, Stockholm, Sweden, 24-28 August, 2014. Washington, DC, United States: I E E E Computer Society. doi: 10.1109/ICPR.2014.452

  • Classifying anti-nuclear antibodies HEp-2 images: A benchmarking platform

    Hobson, Peter, Lovell, Brian C., Percannella, Gennaro, Vento, Mario and Wiliem, Arnold (2014). Classifying anti-nuclear antibodies HEp-2 images: A benchmarking platform. 22nd International Conference on Pattern Recognition, ICPR 2014, Stockholm, Sweden, 24 - 28 August 2014. Washington, DC United States: I E E E Computer Society. doi: 10.1109/ICPR.2014.557

  • Discovering discriminative cell attributes for HEp-2 specimen image classification

    Wiliem, Arnold, Hobson, Peter and Lovell, Brian C. (2014). Discovering discriminative cell attributes for HEp-2 specimen image classification. IEEE Winter Conference on Applications of Computer Vision (WACV 2014), Steamboat Springs, CO, United States, 24-26 March 2014. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/WACV.2014.6836071

  • Domain adaptation on the statistical manifold

    Baktashmotlagh, Mahsa, Harandi, Mehrtash T., Lovell, Brian C. and Salzmann, Mathieu (2014). Domain adaptation on the statistical manifold. 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, OH, United States, 23-28 June 2014. Piscataway, NJ, United States: I E E E Computer Society. doi: 10.1109/CVPR.2014.318

  • Matching image sets via adaptive multi convex hull

    Chen, Shaokang, Wiliem, Arnold, Sanderson, Conrad and Lovell, Brian C. (2014). Matching image sets via adaptive multi convex hull. IEEE Winter Conference on Applications of Computer Vision (WACV 2014), Steamboat Springs, CO, United States, 24-26 March 2014. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/WACV.2014.6835985

  • Multi-Action Recognition via Stochastic Modelling of Optical Flow and Gradients

    Carvajal, Johanna, Sanderson, Conrad, McCool, Chris and Lovell, Brian C. (2014). Multi-Action Recognition via Stochastic Modelling of Optical Flow and Gradients. MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis, Gold Coast , QLD, Australia, 2 December 2014. New York, NY USA: ACM. doi: 10.1145/2689746.2689748

  • Object tracking via non-Euclidean geometry: A Grassmann approach

    Shiraz, Sareh, Harandi, Mehrtash T., Lovell, Brian C. and Sanderson, Conrad (2014). Object tracking via non-Euclidean geometry: A Grassmann approach. IEEE Winter Conference on Applications of Computer Vision (WACV 2014), Steamboat Springs, CO, United States, 24-26 March 2014. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/WACV.2014.6836008

  • Random projections on manifolds of symmetric positive definite matrices for image classification

    Alavi, Azadeh, Wiliem, Arnold, Zhao, Kun, Lovell, Brian C. and Sanderson, Conrad (2014). Random projections on manifolds of symmetric positive definite matrices for image classification. IEEE Winter Conference on Applications of Computer Vision (WACV 2014), Steamboat Springs, CO, United States, 24-26 March 2014. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/WACV.2014.6836085

  • Classification of human epithelial type 2 cell indirect immunofluoresence images via codebook based descriptors

    Wiliem, Arnold, Wong, Yongkang, Sanderson, Conrad, Hobson, Peter, Chen, Shaokang and Lovell, Brian C. (2013). Classification of human epithelial type 2 cell indirect immunofluoresence images via codebook based descriptors. 2013 IEEE Workshop on Applications of Computer Vision (WACV), Tampa, FL, United States, 15-17 Janunary 2013. Piscataway, NJ, USA: IEEE (Institute for Electrical and Electronic Engineers). doi: 10.1109/WACV.2013.6475005

  • Dictionary earning and sparse coding on Grassmann manifolds: an extrinsic solution

    Harandi, Mehrtash, Sanderson, Conrad, Shen, Chunhua and Lovell, Brian C. (2013). Dictionary earning and sparse coding on Grassmann manifolds: an extrinsic solution. IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, 1-8 December 2013. New York, NY United States: IEEE. doi: 10.1109/ICCV.2013.387

  • Improved image set classification via joint sparse approximated nearest subspaces

    Chen, Shaokang, Sanderson, Conrad, Harandi, Mehrtash T and Lovell, Brian C. (2013). Improved image set classification via joint sparse approximated nearest subspaces. 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013, Portland, OR United States, 23 - 28 June 2013. Piscataway, NJ United States: I E E E. doi: 10.1109/CVPR.2013.65

  • Non-linear stationary subspace analysis with application to video classification

    Baktashmotlagh, Mahsa, Harandi, Mehrtash T., Bigdeli, Abbas, Lovell, Brian C. and Salzmann, Mathieu (2013). Non-linear stationary subspace analysis with application to video classification. 30th International Conference on Machine Learning, Atlanta, GA, United States, 16 - 21 June 2013. Germany: International Machine Learning Society (IMLS).

  • Region-based anomaly localisation in crowded scenes via trajectory analysis and path prediction

    Zhang, Teng, Wiliem, Arnold and Lovell, Brian C. (2013). Region-based anomaly localisation in crowded scenes via trajectory analysis and path prediction. 2013 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2013, Hobart, TAS, Australia, November 26, 2013-November 28, 2013. Piscataway, NJ, United States: IEEE. doi: 10.1109/DICTA.2013.6691519

  • Spatio-temporal covariance descriptors for action and gesture recognition

    Sanin, Andres, Sanderson, Conrad, Harandi, Mehrtrash and Lovell, Brian (2013). Spatio-temporal covariance descriptors for action and gesture recognition. 2013 IEEE Workshop on Applications of Computer Vision, Tampa, FL, United States, 15-17 January 2013. Piscataway, NJ, United States: IEEE (Institute for Electrical and Electronic Engineers). doi: 10.1109/WACV.2013.6475006

  • Unsupervised domain adaptation by Domain Invariant Projection

    Baktashmotlagh, Mahsa, Harandi, Mehrtash T., Lovell, Brian C. and Salzmann, Mathieu (2013). Unsupervised domain adaptation by Domain Invariant Projection. 2013 IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, 1-8 December 2013. Piscataway, NJ, United States: IEEE. doi: 10.1109/ICCV.2013.100

  • A wireless mesh sensor network for hazard and safety monitoring at the Port of Brisbane

    Ahmadi, Amin, Bigdeli, Abbas, Baktashmotlagh, Mahsa and Lovell, Brian C. (2012). A wireless mesh sensor network for hazard and safety monitoring at the Port of Brisbane. 37th Annual IEEE Conference on Local Computer Networks (LCN 2012), Clearwater, FL, United States, 22-25 October 2012. Washington, DC, United States: IEEE. doi: 10.1109/LCN.2012.6423601

  • Clustering on Grassmann manifolds via kernel embedding with application to action analysis

    Shirazi, Sareh, Harandi, Mehrtash T., Sanderson, Conrad, Alavi, Azadeh and Lovell, Brian C. (2012). Clustering on Grassmann manifolds via kernel embedding with application to action analysis. 2012 19th IEEE International Conference on Image Processing (ICIP), Orlando, United States, 30 September - 3 October 2012. Piscataway, NJ, United States: IEEE. doi: 10.1109/ICIP.2012.6466976

  • Combined learning of salient local descriptors and distance metrics for image set face verification

    Sanderson, Conrad, Harandi, Mehrtash T., Wong, Yongkang and Lovell, Brian C. (2012). Combined learning of salient local descriptors and distance metrics for image set face verification. 9th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), Beijing, Peoples R China, 18-21 September 2012. Los Alamitos, CA United States: I E E E Computer Society. doi: 10.1109/AVSS.2012.23

  • Directional space-time oriented gradients for 3D visual pattern analysis

    Norouznezhad, Ehsan, Harandi, Mehrtash T., Bigdeli, Abbas, Baktash, Mahsa, Postula, Adam and Lovell, Brian C. (2012). Directional space-time oriented gradients for 3D visual pattern analysis. 12th European Conference on Computer Vision, ECCV 2012, Florence, Italy, 7 - 13 October 2012. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-33712-3_53

  • Gaussian probabilistic confidence score for biometric applications

    Mau, Sandra, Dadgostar, Farhad and Lovell, Brian C. (2012). Gaussian probabilistic confidence score for biometric applications. 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA), Fremantle, WA, Australia, 3-5 December 2012. Piscataway, NJ, United States: IEEE. doi: 10.1109/DICTA.2012.6411712

  • Improved person re-identification using statistical approximation

    Yang, Yan, Dadgostar, Farhad, Mau, Sandra and Lovell, Brian C. (2012). Improved person re-identification using statistical approximation. 2012 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Fremantle, WA, Australia, 3-5 December 2012. Piscataway, NJ, United States: IEEE. doi: 10.1109/DICTA.2012.6411683

  • K-tangent spaces on Riemannian manifolds for improved pedestrian detection

    Sanin, Andres, Sanderson, Conrad, Harandi, Mehrtash and Lovell, Brian C. (2012). K-tangent spaces on Riemannian manifolds for improved pedestrian detection. 2012 19th IEEE International Conference on Image Processing (ICIP), Orlando, United States, 30 September - 3 October 2012. Piscataway, NJ, United States: IEEE. doi: 10.1109/ICIP.2012.6466899

  • Kernel analysis over Riemannian manifolds for visual recognition of actions, pedestrians and textures

    Harandi, Mehrtash T., Sanderson, Conrad, Wiliem, Arnold and Lovell, Brian C. (2012). Kernel analysis over Riemannian manifolds for visual recognition of actions, pedestrians and textures. 2012 IEEE Workshop on Applications of Computer Vision, Breckenridge, CO, United States, 9-11 January 2012. Piscataway, NJ, United States: IEEE (Institute for Electrical and Electronic Engineers). doi: 10.1109/WACV.2012.6163005

  • On robust biometric identity verification via sparse encoding of faces: Holistic vs local approaches

    Wong, Yongkang, Harandi, Mehrtash T., Sanderson, Conrad and Lovell, Brian C. (2012). On robust biometric identity verification via sparse encoding of faces: Holistic vs local approaches. WCCI 2012 IEEE World Congress on Computational Intelligence, Brisbane Australia, 10-15 June 2012. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/IJCNN.2012.6252611

  • Role of spatiotemporal oriented energy features for robust visual tracking in video surveillance

    Emami, Ali, Dadgostar, Farhad, Bigdeli, Abbas and Lovell, Brian C. (2012). Role of spatiotemporal oriented energy features for robust visual tracking in video surveillance. 9th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), Beijing, Peoples R China, 18-21 September 2012. Los Alamitos, CA United States: I E E E Computer Society. doi: 10.1109/AVSS.2012.64

  • Sparse coding and dictionary learning for symmetric positive definite matrices: a kernel approach

    Harandi,Mehrtash T., Sanderson, Conrad, Hartley, Richard and Lovell, Brian C. (2012). Sparse coding and dictionary learning for symmetric positive definite matrices: a kernel approach. 12th European Conference on Computer Vision, Florence, Italy, 7-13 October, 2012. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-33709-3_16

  • A face biometric benchmarking review and characterisation

    Mau, Sandra, Dadgostar, Farhad, Cullinan, Ian, Bigdeli, Abbas and Lovell, Brian C. (2011). A face biometric benchmarking review and characterisation. IEEE International Conference on Computer Vision Workshops (ICCV Workshops), Barcelona, Spain, 6-13 November 2011. Piscataway, NJ, United States: IEEE. doi: 10.1109/ICCVW.2011.6130510

  • An appearance-based approach to assistive identity inference using LBP and colour histograms

    Shirazi, Sareh Abolahrari, Dadgostar, Farhad and Lovell, Brian C. (2011). An appearance-based approach to assistive identity inference using LBP and colour histograms. International Workshops on Computer Vision, ACCV 2010, Queenstown, New Zealand, 8-9 November 2010. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-22822-3_24

  • An automatic image based single dilution method for end point titre quantitation of antinuclear antibodies tests using HEp-2 cells

    Wiliem, Arnold, Hobson, Peter, Minchin, Rodney F. and Lovell, Brian C. (2011). An automatic image based single dilution method for end point titre quantitation of antinuclear antibodies tests using HEp-2 cells. 2011 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2011), Noosa, QLD, Australia, 6 - 8 December 2011. Piscatawa, NJ, United States: IEEE Computer Society. doi: 10.1109/DICTA.2011.9

  • Dynamic resource aware sensor networks: Integration of sensor cloud and ERPs

    Baktashmotlagh, Mahsa, Bigdeli, Abbas and Lovell, Brian C. (2011). Dynamic resource aware sensor networks: Integration of sensor cloud and ERPs. 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), 2011, Klagenfurt, Austria, 30 August - 2 September 2011. Piscataway, NJ, United States: IEEE. doi: 10.1109/AVSS.2011.6027375

  • Ensemble of furthest subspace pairs for enhanced image set matching

    Harandi, Mehrtash T., Sanderson, Conrad, Bigdeli, Abbas and Lovell, Brian C. (2011). Ensemble of furthest subspace pairs for enhanced image set matching. 18th IEEE International Conference on Image Processing (ICIP), Brussels, Belguim, 11-14 September 2011. Piscataway, NJ, United States: I E E E. doi: 10.1109/ICIP.2011.6116683

  • Graph embedding discriminant analysis on Grassmannian manifolds for improved image set matching

    Harandi, Mehrtash T., Sanderson, Conrad, Shirazi, Sareh and Lovell, Brian C. (2011). Graph embedding discriminant analysis on Grassmannian manifolds for improved image set matching. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, USA, 20-25 June 2011. Piscataway, NJ, United States: IEEE. doi: 10.1109/CVPR.2011.5995564

  • Improved anomaly detection in crowded scenes via cell-based analysis of foreground speed, size and texture

    Reddy, Vikas, Sanderson, Conrad and Lovell, Brian C. (2011). Improved anomaly detection in crowded scenes via cell-based analysis of foreground speed, size and texture. 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Colorado Springs, CO, United States, 20-25 June 2011. Piscataway, NJ, United States: IEEE. doi: 10.1109/CVPRW.2011.5981799

  • Invited paper: Embedded face and biometric technologies for national and border security

    Lovell, Brian C., Bigdeli, Abbas and Mau, Sandra (2011). Invited paper: Embedded face and biometric technologies for national and border security. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2011, Colorado Springs, CO, United States, 20-25 June 2011. Piscataway, NJ, United States: IEEE. doi: 10.1109/CVPRW.2011.5981830

  • MRF-based background initialisation for improved foreground detection in cluttered surveillance videos

    Reddy, Vikas, Sanderson, Conrad, Sanin, Andres and Lovell, Brian C. (2011). MRF-based background initialisation for improved foreground detection in cluttered surveillance videos. Asian Conference on Computer Vision, Queenstown, New Zealand, 8-12 November 2010. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-19318-7_43

  • Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition

    Wong, Yongkang, Chen, Shaokang, Mau, Sandra, Sanderson, Conrad and Lovell, Brian C. (2011). Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition. 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Colorado Springs, CO, United States, 20-25 June 2011. Piscataway, NJ, United States: IEEE. doi: 10.1109/CVPRW.2011.5981881

  • Summarisation of surveillance videos by key-frame selection

    Yang, Yan, Dadgostar, Farhad, Sanderson, Conrad and Lovell, Brian C. (2011). Summarisation of surveillance videos by key-frame selection. Fifth ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC), Ghent, Belgium, 22-25 August 2011. Piscataway, NJ, United States: IEEE. doi: 10.1109/ICDSC.2011.6042925

  • A Bayesian network-based framework with Constraint Satisfaction Problem (CSP) formulations for FPGA system design

    Azman, .Amelia W., Bigdeli, Abbas, Mohd-Mustafah, Yasir, Biglari-Abhari, Morteza and Lovell, Brian C. (2010). A Bayesian network-based framework with Constraint Satisfaction Problem (CSP) formulations for FPGA system design. 21st IEEE International Conference on Application-specific Systems, Architectures and Processors, ASAP 2010, Rennes, France, 7 - 9 July 2010. Washington, DC United States: I E E E Computer Society. doi: 10.1109/ASAP.2010.5540784

  • A framework for lab-based real-time video analysis on distributed camera networks

    Dadgostar, Farhad, Bigdeli, Abbas, Mau, Sandra, Smith, Terence and Lovell, Brian (2010). A framework for lab-based real-time video analysis on distributed camera networks. 4th ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2010, Atlanta, GA United States, 31 August - 4 September 2010. New York, NY United States: ACM (Association for Computing Machinery) Press. doi: 10.1145/1865987.1866028

  • Adaptive patch-based background modelling for improved foreground object segmentation and tracking

    Reddy, Vikas, Sanderson, Conrad, Sanin, Andres and Lovell, Brian (2010). Adaptive patch-based background modelling for improved foreground object segmentation and tracking. 2010 7th International Conference on Advanced Video and Signal-Based Surveillance, Boston, MA, United States, 29 August - 1 September 2010. Piscataway, NJ, United States: IEEE Computer Society. doi: 10.1109/AVSS.2010.84

  • Directed random subspace method for face recognition

    Harandi, Mehrtash T., Ahmadabadi, Majid Nili, Araabi, Babak Nadjar, Bigdeli, Abbas and Lovell, Brian C. (2010). Directed random subspace method for face recognition. ICPR 2010: 20th International Conference on Pattern Recognition, Istanbul, Turkey, 23-26 August 2010. Piscataway, NJ, U.S.A.: IEEE - Institute for Electrical and Electronic Engineers. doi: 10.1109/ICPR.2010.659

  • Dynamic amelioration of resolution mismatches for local feature based identity inference

    Wong, Yongkang, Sanderson, Conrad, Mau, Sandra and Lovell, Brian C. (2010). Dynamic amelioration of resolution mismatches for local feature based identity inference. 2010 20th International Conference on Pattern Recognition (ICPR 2010), Istanbul, Turkey, 23-26 August 2010. Washington, DC, U.S.A.: IEEE Computer Society. doi: 10.1109/ICPR.2010.299

  • Feature space Hausdorff distance for face recognition

    Chen, Shaokang and Lovell, Brian C. (2010). Feature space Hausdorff distance for face recognition. 20th International Conference on Pattern Recognition (ICPR), 2010, Istanbul, Turkey, 23-26 August 2010. Piscataway, NJ, United States: IEEE (Institute for Electrical and Electronic Engineers). doi: 10.1109/ICPR.2010.362

  • Image-set face recognition based on transductive learning

    Harandi, Mehrtash T., Bigdeli, Abbas and Lovell, Brian C. (2010). Image-set face recognition based on transductive learning. IEEE International Conference on Image Processing, Hong Kong, 26-29 September 2010. Piscataway, NJ, United States: IEEE - Computer Society. doi: 10.1109/ICIP.2010.5651105

  • Improved shadow removal for robust person tracking in surveillance scenarios

    Sanin, Andres, Sanderson, Conrad and Lovell, Brian (2010). Improved shadow removal for robust person tracking in surveillance scenarios. International Conference on Pattern Recognition (20th, ICPR 2010), Istanbul, Turkey, 23-26 August 2010. Los Alamitos, CA, United States: IEEE. doi: 10.1109/ICPR.2010.43

  • Object tracking on FPGA-based smart cameras using local oriented energy and phase features

    Norouznezhad, Ehsan, Bigdeli, Abbas, Postula, Adam and Lovell, Brian C. (2010). Object tracking on FPGA-based smart cameras using local oriented energy and phase features. International Conference on Distributed Smart Cameras (ICDSC 2010), Atlanta, GA, United States, 31 August - 4 September 2010. New York, United States: ACM. doi: 10.1145/1865987.1865993

  • Robust foreground object segmentation via adaptive region-based background modelling

    Reddy, Vikas, Sanderson, Conrad and Lovell, Brian C. (2010). Robust foreground object segmentation via adaptive region-based background modelling. 2010 20th International Conference on Pattern Recognition (ICPR 2010), Istanbul, Turkey, 23-26 August 2010. Washington, DC, U.S.A.: IEEE Computer Society. doi: 10.1109/ICPR.2010.958

  • Robust object tracking using local oriented energy features and its hardware/software implementation

    Bigdeli, Abbas, Postula, Adam, Lovell, Brian C. and Norouznezhad, Ehsan (2010). Robust object tracking using local oriented energy features and its hardware/software implementation. 11th International Conference on Control Automation Robotics & Vision (ICARCV), 2010, Singapore, 7 - 10 December 2010. Piscataway, NJ, United States: IEEE (Institute for Electrical and Electronic Engineers). doi: 10.1109/ICARCV.2010.5707853

  • Square patch feature based face detection architecture for high resolution smart camera

    Mustafah, Yasir M., Bigdeli, Abbas, Azman, Amelia W. and Lovell, Brian C. (2010). Square patch feature based face detection architecture for high resolution smart camera. 4th ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2010, Atlanta, GA United States, 31 August - 4 September 2010. New York, NY United States: ACM (Association for Computing Machinery) Press. doi: 10.1145/1865987.1866015

  • Square patch feature: Faster weak-classifier for robust object detection

    Mustafah, Yasir M., Bigdeli, Abbas, Azman, Amelia W., Dadgostar, Farhad and Lovell, Brian C. (2010). Square patch feature: Faster weak-classifier for robust object detection. 11th International Conference on Control Automation Robotics & Vision (ICARCV), 2010, Singapore, 7-10 December 2010. Piscataway, NJ, United States: IEEE (Institute for Electrical and Electronic Engineers). doi: 10.1109/ICARCV.2010.5707809

  • Video face matching using subset selection and clustering of probabilistic multi-region histograms

    Mau, Sandra, Chen, Shaokang, Sanderson, Conrad and Lovell, Brian C. (2010). Video face matching using subset selection and clustering of probabilistic multi-region histograms. 25th International Conference of Image and Vision Computing (IVCNZ), Queenstown, New Zealand, 8 - 9 November 2010. Washington, DC, United States: IEEE Computer Society. doi: 10.1109/IVCNZ.2010.6148860

  • A BBN-based framework for adaptive IP-reuse

    Azman, Amelia W, Bigdeli, Abbas, Biglari-Abhari, Morteza, Mustafah, Yasir M. and Lovell B.C. (2009). A BBN-based framework for adaptive IP-reuse. 6th FPGAworld Conference, FPGAworld 2009, Stockholm, Sweden, 10 September 2009. New York , NY United States: ACM (Association for Computing Machinery) Press. doi: 10.1145/1667520.1667521

  • An abandoned object detection system based on dual background segmentation

    Singh, A., Sawan, S., Hanmandlu, M., Madasu, V. K. and Lovell, B. C. (2009). An abandoned object detection system based on dual background segmentation. AVSS 2009: 6th IEEE International Conference on Advanced Video and Signal Based Surveillance, Genoa, Italy, 2-4 September 2009. Los Alamitos, CA, U.S.A.: Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/AVSS.2009.74

  • An efficient and robust sequential algorithm for background estimation in video surveillance

    Reddy, Vikas, Sanderson, Conrad and Lovell, Brian, C. (2009). An efficient and robust sequential algorithm for background estimation in video surveillance. International Conference on Image Processing (ICIP 2009), Cairo, Egypt, 7-12 November 2009. Piscataway NJ, United States: IEEE. doi: 10.1109/ICIP.2009.5413450

  • An efficient background estimation algorithm for embedded smart cameras

    Reddy, Vikas, Sanderson, Conrad, Lovell, Brian, C. and Bigdeli, Abbas (2009). An efficient background estimation algorithm for embedded smart cameras. 3rd ACM/IEEE International Conference on Distributed Smart Cameras, Como, Italy, 30 August 2009 - 2 September 2009. Piscataway NJ, USA: IEEE. doi: 10.1109/ICDSC.2009.5289348

  • Biometric Authentication Based on Infrared Thermal Hand Vein Patterns

    Kumar, A., Hanmandlu, M., Madasu, V. K. and Lovell, B. C. (2009). Biometric Authentication Based on Infrared Thermal Hand Vein Patterns. Digital Image Computing: Techniques and Applications, 2009. DICTA '09, Melbourne, Victoria, Australia, 1-3 December, 2009. Los Alamitos, California: IEEE. doi: 10.1109/DICTA.2009.63

  • Blotch detection in pigmented skin lesions using fuzzy co-clustering and texture segmentation

    Madasu, Vamsi K. and Lovell, Brian C. (2009). Blotch detection in pigmented skin lesions using fuzzy co-clustering and texture segmentation. DICTA 2009, Melbourne , Australia, 1-3 December 2009. New Jersey, U.S.A.: CPS Publishing Services. doi: 10.1109/DICTA.2009.15

  • Content-Based Video Retrieval (CBVR) system for CCTV surveillance videos

    Yang, Yan, Lovell, Brian C. and Dadgostar, Farhad (2009). Content-Based Video Retrieval (CBVR) system for CCTV surveillance videos. Digital Image Computing: Techniques and Applications, DICTA 2009, Melbourne, VIC Australia, 1 - 3 December 2009. Piscataway, NJ United States: I E E E. doi: 10.1109/DICTA.2009.36

  • Detection of skin lesions by fuzzy entropy based texel identification

    Susan, Seba, Hanmandlu, M., Madasu, Vamsi K. and Lovell, Brian C. (2009). Detection of skin lesions by fuzzy entropy based texel identification. ISPA 2009, Salzburg, Austria, 16-18 September 2009. Salzburg, Austria: University of Salzburg.

  • Exploiting Bayesian belief network for adaptive IP-reuse decision

    Azman, A. W., Bigdeli, A., Biglari-Abhari, M., Mustafah, Y. M. and Lovell, B. C. (2009). Exploiting Bayesian belief network for adaptive IP-reuse decision. Digital Image Computing: Techniques and Applications, DICTA 2009, Melbourne, VIC Australia, 1 - 3 December 2009. Piscataway, NJ United States: I E E E. doi: 10.1109/DICTA.2009.21

  • Face detection system design for real time high resolution smart camera

    Mustafah, Yasir M., Bigdeli, Abbas, Azman, Amelia W. and Lovell, Brian C. (2009). Face detection system design for real time high resolution smart camera. 3rd ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2009, Como, Italy, 30 August - 2 September 2009. Piscataway, NJ United States: I E E E. doi: 10.1109/ICDSC.2009.5289346

  • Fusion of hand based biometrics using ant colony optimization

    Madasu, Vamsi K., Lovell, Brian C. and Vasikarla, Shantaram (2009). Fusion of hand based biometrics using ant colony optimization. AIPR 2009. Vision: Humans, Animals, and Machines. 38th Applied Imagery Pattern Recognition Workshop, Washington, DC, U.S.A., 14-16 October 2009.

  • Multi-region probabilistic histograms for robust and scalable identity inference

    Sanderson, Conrad and Lovell, Brian C. (2009). Multi-region probabilistic histograms for robust and scalable identity inference. 3rd IAPR/IEEE International Conference on Biometrics, Alghero, Italy, 2-5 June 2009. Berlin / Heidelberg: Springer Verlag. doi: 10.1007/978-3-642-01793-3_21

  • Regression based non-frontal face synthesis for improved identity verification

    Wong, Yong, Sanderson, Conrad and Lovell, Brian C. (2009). Regression based non-frontal face synthesis for improved identity verification. 13th International Conference, CAIP 2009, Munster, Germany, 2-4 September, 2009. Heidelberg, Germany: Springer-Verlag. doi: 10.1007/978-3-642-03767-2_14

  • Representative feature chain for single gallery image face recognition

    Chen, Shaokang, Sanderson, Conrad, Sun, Sai and Lovell, Brian (2009). Representative feature chain for single gallery image face recognition. 19th International Conference on Pattern Recognition, Tampa, Florida, USA, 8-11 December, 2008. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICPR.2008.4760975

  • Self occlusions and graph based edge measurement schemes for visual tracking applications

    Smith, Andrew W. B. and Lovell, Brian C. (2009). Self occlusions and graph based edge measurement schemes for visual tracking applications. Digital Image Computing: Techniques and Applications, DICTA 2009, Melbourne, VIC Australia, 1 - 3 December 2009. Piscataway, NJ United States: I E E E. doi: 10.1109/DICTA.2009.74

  • Tracking of persons for video surveillance of unattended environments

    Kong, Suyu, Bhuyan, M. K., Sanderson, C. and Lovell, Brian C. (2009). Tracking of persons for video surveillance of unattended environments. ICPR 2008: 19th International Conference on Pattern Recognition, Tampa, FL, USA, 8-11 December, 2008. Washington, DC, United States: I E E E Computer Society. doi: 10.1109/ICPR.2008.4761338

  • A high resolution smart camera with GigE Vision extension for surveillance applications

    Norouznezhad, E., Bigdeli, A., Postula, A. and Lovell, B. C. (2008). A high resolution smart camera with GigE Vision extension for surveillance applications. 2nd ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC’08), Palo Alto, CA, U.S.A., 7-11 September 2008. Piscataway, NJ, U.S.A.: IEEE Xplore. doi: 10.1109/ICDSC.2008.4635711

  • Experimental analysis of face recognition on still and CCTV images

    Chen, Shaokang, Berglund, Erik, Bigdeli, Abbas, Sanderson, Conrad and Lovell, Brian C. (2008). Experimental analysis of face recognition on still and CCTV images. IEEE International Conference on Advanced Video and Signal Based Surveillance 2008 (AVSS '08), Santa Fe, New Mexico, U.S.A., 1-3 September 2008. Piscataway, NJ, United States: IEEE - Institute of Electrical Electronics Engineers Inc.. doi: 10.1109/AVSS.2008.15

  • Fuzzy Co-Clustering of Medical Images using Bacterial Foraging

    Hanmandlu, M., Susan, S., Madasu, V.K. and Lovell, B.C. (2008). Fuzzy Co-Clustering of Medical Images using Bacterial Foraging. Image and Vision Computing New Zealand 2008, Lincoln University, New Zealand, 26-28 November, 2008. USA: IEEE. doi: 10.1109/IVCNZ.2008.4762136

  • On Intelligent Surveillance Systems and Face Recognition for Mass Transport Security

    Lovell, Brian C., Chen, Shaokang, Bigdeli, Abbas, Berglund, Erik and Sanderson, Conrad (2008). On Intelligent Surveillance Systems and Face Recognition for Mass Transport Security. International Conference on Control, Automation, Robotics and Vision (ICARCV), 2008., Hanoi, Vietnam, 17 - 20 December 2008. Piscataway, NJ, United States: IEEE. doi: 10.1109/ICARCV.2008.4795605

  • Proceedings of the 1st International Conference on Advances in Computer-Human Interaction, ACHI 2008: Preface

    Dascalu, Sergiu, Wang, Alf Inge, Dragan, Irinel C., Ge, Shuzhi Sam, Nakashima, Tomoharu, Milani, Alfredo, Lovell, Brian C., Viniotis, Yannis, Latombe, Jean-Claude, Nearchou, Andreas C., Oinas-Kukkonen, Harri and Zaytoon, Janan (2008). Proceedings of the 1st International Conference on Advances in Computer-Human Interaction, ACHI 2008: Preface. First International Conference on Advances in Computer-Human Interaction, Saint Luce, Martinique, 10-15 February 2008. doi: 10.1109/ACHI.2008.4

  • Tracking with Multiple Cameras for Video Surveillance

    Bhuyan, M. K., Lovell, B. C. and Bigdeli, A. (2008). Tracking with Multiple Cameras for Video Surveillance. 9th Biennial Conference of the Australian Pattern Recognition Society, Glenelg, SA, Australia, 3-5 December 2007. Glenelg, SA, Australia: IEEE Computer Society. doi: 10.1109/DICTA.2007.4426852

  • An automated face recognition system for intelligence surveillance: Smart camera recognizing faces in the crowd

    Mustafah, Y. M., Azman, A., Bigdeli, A. and Lovell, B. C. (2007). An automated face recognition system for intelligence surveillance: Smart camera recognizing faces in the crowd. First ACM/IEEE International Conference on Distributed Smart Cameras 2007, Vienna, Austria, 25-28 September 2007. Piscataway, NJ, U.S.A.: IEEE - Institute of Electrical Electronics Engineers Inc.. doi: 10.1109/ICDSC.2007.4357518

  • Classifying and tracking multiple persons for proactive surveillance of mass transport systems

    Kong, Suyu, Sanderson, C. and Lovell, Brian C. (2007). Classifying and tracking multiple persons for proactive surveillance of mass transport systems. IEEE Conference on Advanced Video and Signal Based Surveillance 2007 (AVSS2007), London, UK, 5-7 September 2007. Piscataway, NJ, USA: IEEE. doi: 10.1109/AVSS.2007.4425303

  • Combining classifiers in rotated face space

    Chen S., Shan T. and Lovell B.C. (2007). Combining classifiers in rotated face space. Australian Pattern Recognition Society (APRS), Glenelg, SA, December 3, 2007-December 5, 2007. IEEE. doi: 10.1109/DICTA.2007.4426822

  • Face Detection on Embedded Systems

    Bigdeli, Abbas, Sim, Colin, Biglari-Abhari, Morteza and Lovell Brian C. (2007). Face Detection on Embedded Systems. Third International Conference on Embedded Software and Systems, Daegu, Korea, 14-16 May 2007. Berlin, Heidelberg: Springer-Verlag. doi: 10.1007/978-3-540-72685-2_28

  • Optimizing resources of an FPGA-based smart camera architecture

    Azman, A. W., Bigdeli, A., Mustafah, Y. M. and Lovell, B. C. (2007). Optimizing resources of an FPGA-based smart camera architecture. 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA 2007), Adelaide, Australia, 3-5 December 2007. Piscataway, NJ, U.S.A.: IEEE- Institute of Electrical Electronics Engineers Inc.. doi: 10.1109/DICTA.2007.4426853

  • Real-time face detection and tracking for high resolution smart camera system

    Mustafah, Y. M., Shan, T., Azman, A., Bigdeli, A. and Lovell, B. C. (2007). Real-time face detection and tracking for high resolution smart camera system. 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA 2007), Adelaide, Australia, 3-5 December 2007. Piscataway, NJ, U.S.A.: IEEE - Institute of Electrical Electronics Engineers Inc.. doi: 10.1109/DICTA.2007.4426823

  • Robust Face Recognition in Rotated Eigen Space

    Chen, S., Shan, T. and Lovell, B. C. (2007). Robust Face Recognition in Rotated Eigen Space. The Twenty-second International Image and Vision Computing New Zealand Conference, Hamilton, N.Z, 5-7th December.

  • Smart cameras enabling automated face recognition in the crowd for intelligent surveillance system

    Mustafah, Y.M., Bigdeli, A., Azman, A.W and Lovell, B.C. (2007). Smart cameras enabling automated face recognition in the crowd for intelligent surveillance system. Recent Advances in Security Technology (RNSA) 2007, Melbourne, Australia, 28 September, 2007. Curtin, ACT: Australian Homeland Security Research Centre.

  • Towards Pose-Invariant 2D Face Classification for Surveillance

    Conrad Sanderson, Ting Shan and Brian C. Lovell (2007). Towards Pose-Invariant 2D Face Classification for Surveillance. Analysis and Modeling of Faces and Gestures Third InternationalWorkshop (AMFG 2007), Rio de Janeiro, Brazil, 20 October 2007. Heidelberg, Germany: Springer. doi: 10.1007/978-3-540-75690-3_21

  • Towards robust face recognition for intelligent-CCTV based surveillance using one gallery image

    Shan, Ting, Chen, Shaokang, Sanderson, Conrad and Lovell, Brian C. (2007). Towards robust face recognition for intelligent-CCTV based surveillance using one gallery image. IEEE Conference on Advanced Video and Signal Based Surveillance, 2007 (AVSS 2007), London, United Kingdom, 5-7 September 2007. Piscataway, NJ, United States: IEEE. doi: 10.1109/ICICIC.2007.377

  • Vision Processing in Intelligent CCTV for Mass Transport Security

    Bigdeli A., Lovell B. C., Sanderson C., Shan, T. and Chen S. (2007). Vision Processing in Intelligent CCTV for Mass Transport Security. SAFE 2007: Workshop on Signal Processing Applications for Public Security and Forensics, Washington, DC, USA,, 11-13 April 2007. Washington, DC, USA,: IEEE Signal Processing Society.

  • Face recognition robust to head pose from one sample image

    Shan, Ting, Lovell, Brian C. and Chen, Shaokang (2006). Face recognition robust to head pose from one sample image. 18th International Conference on Pattern Recogntion (ICPR 2006), Hong Kong, 20-24 August 2006. Washington, DC, United States: IEEE. doi: 10.1109/ICPR.2006.527

  • Measurement Function Design for Visual Tracking Applications

    Smith, A. W. B. and Lovell, B. C. (2006). Measurement Function Design for Visual Tracking Applications. 18th International Conference on Pattern Recognition (ICPR 2006), Hong Kong, 20-24 August, 2006. U.S.A.: IEEE. doi: 10.1109/ICPR.2006.785

  • Real-time quantitative bronchoscopy

    Wilson, Simon, Lovell, Brian, Anne, Chang and Masters, Brent (2005). Real-time quantitative bronchoscopy. Digital Imaging Computing: Techniques and Applications, DICTA 2005, , , December 6, 2005-December 8, 2005. doi: 10.1109/DICTA.2005.1578149

  • A Study of the Optimality of Approximate Maximum Likelihood Estimation

    McKinnon, D. N. R. and Lovell, B. C. (2005). A Study of the Optimality of Approximate Maximum Likelihood Estimation. APRS Workshop on Digital Image Computing (WFIC 2005), Brisbane, Australia, 21 February, 2005. Brisbane, Australia: The University of Queensland.

  • Arrhythmia detection in human electrocardiogram

    Chiranjivi, G. V. S., Madasu, Vamsi Krishna, Hanmandlu, Madasu and Lovell, Brian C. (2005). Arrhythmia detection in human electrocardiogram. APRS Workshop on Digital Image Computing, Brisbane, 21 February, 2005. Brisbane, Australia: The University of Queensland.

  • Automatic Handwritten Signature Verification System for Australian Passports

    Madasu, Vamsi K., Lovell, Brian C. and Kubik, Kurt (2005). Automatic Handwritten Signature Verification System for Australian Passports. Science, Engineering and Technology Summit on Counter-Terrorism Technology, Canberra, 14 July, 2005.

  • Automatic Segmentation and Recognition of Bank Cheque Fields

    Madasu, Vamsi K. and Lovell, Brian C. (2005). Automatic Segmentation and Recognition of Bank Cheque Fields. Digital Image Computing: Techniques and Applications (DICTA 20005), Cairns, 6-8 December, 2005. Los Altimos, CA: IEEE CS Press. doi: 10.1109/DICTA.2005.1578131

  • Combining Generative and Discriminative Learning for Face Recognition

    Chen, Shaokang, Lovell, Brian C. and Shan, Ting (2005). Combining Generative and Discriminative Learning for Face Recognition. Digital Image Computing: Techniques and Applications (DICTA 2005), Cairns, 6-8 December, 2005. Los Alamitos, CA, USA: IEEE CS Press. doi: 10.1109/DICTA.2005.1578103

  • Globally optimal 3D image reconstruction and segmentation via energy minimisation techniques

    Lovell, Brian C. (2005). Globally optimal 3D image reconstruction and segmentation via energy minimisation techniques. 1st International Conference on Pattern Recognition and Machine Intelligence (PReMI2005), Kolkata, India, 18-22 December 2005. Berlin, Germany: Springer. doi: 10.1007/11590316_16

  • Hand Printed Character Recognition Using Neural Networks

    Madasu, Vamsi K., Lovell, Brian C. and Hanmandlu, Madasu (2005). Hand Printed Character Recognition Using Neural Networks. International Conference on Cognition and Recognition, Mysore, India, 22-23 December, 2005.

  • Hand gesture extraction by active shape models

    Liu, Nianjun and Lovell, Brian C. (2005). Hand gesture extraction by active shape models. Digital Image Computing: Techniques and Applications (DICTA 2005), Cairns, Australia, 6-8 December, 2005. Los Alamitos, CA, USA: IEEE CS Press. doi: 10.1109/DICTA.2005.1578108

  • Homogenised Virtual Support Vector Machines

    Walder, C. J. and Lovell, B. C. (2005). Homogenised Virtual Support Vector Machines. Digital Image Computing: Techniques and Applications (DICTA 2005), Cairns, QLD Australia, 6-8 December 2005. Los Alamitos, CA United States: IEEE. doi: 10.1109/DICTA.2005.43

  • Manufacturing Multiple View Constraints

    McKinnon, David N. R. and Lovell, Brian C. (2005). Manufacturing Multiple View Constraints. APRS Workshop on Digital Image Computing, Brisbane, 21 February, 2005. Brisbane: The Australian Pattern Recognition Society.

  • Pedestrian Tracking Based on Colour and Spatial Information

    Seitner, Florian H. and Lovell, Brian C. (2005). Pedestrian Tracking Based on Colour and Spatial Information. Digital Image Computing: Techniques and Applications (DICTA 2005), Cairns, Australia, 6-8 December, 2005. Los Alamitos, CA: IEEE CS Press. doi: 10.1109/DICTA.2005.1578105

  • Person Location Service on the Planetary Sensor Network

    Shan, Ting, Lovell, Brian C. and Chen, Shaokang (2005). Person Location Service on the Planetary Sensor Network. APRS Workshop on Digital Image Computing, Brisbane, 21 February, 2005. Brisbane, Austrlaia: The Australian Pattern Recognition Society.

  • Real-Time Quantitative Bronchoscopy

    Wilson, Simon, Lovell, Brian C., Chang, Anne and Masters, Brent (2005). Real-Time Quantitative Bronchoscopy. Digital Image Computing: Techniques and Applications (DICTA 2005), Cairns, Australia, 6-8 December, 2005. Los Alamitos, CA.: IEEE CS Press.

  • Robust face recognition for data mining

    Lovell, Brian C. and Chen, Shaokang (2005). Robust face recognition for data mining. APRS Workshop on Digital Image Computing, Brisbane, Australia, 21st February, 2005. St Lucia, Australia: The University of Queensland.

  • Visual Odometry for Quantitative Bronchoscopy Using Optical Flow

    Wilson, S., Lovell, B. C., Chang, A. and Masters, B. (2005). Visual Odometry for Quantitative Bronchoscopy Using Optical Flow. APRS Workshop on Digital Image Computing 2005, Brisbane, Australia, 21 February, 2005. Birsbane, Australia: University of Queensland.

  • Visual tracking for sports applications

    Smith, Andrew W. B. and Lovell, Brian C. (2005). Visual tracking for sports applications. APRS Workshop on Digital Image Computing, Brisbane, 21 February, 2005. St Lucia, Australia: The University of Queensland.

  • An Energy Minimisation Approach to Stereo-Temporal Dense Reconstruction

    Leung, C. W., Appleton, B. C., Lovell, B. C. and Sun, C. (2004). An Energy Minimisation Approach to Stereo-Temporal Dense Reconstruction. International Conference on Pattern Recognition, Cambridge, United Kingdom, 23-26 August. Los Alamitos, California: IEEE Computer Society. doi: 10.1109/ICPR.2004.1333708

  • Building Detection by Dempster-Shafer Fusion of LIDAR Data and Multispectral Aerial Imagery

    Rottensteiner, F., Trinder, J., Clode, S., Kubik, K. and Lovell, B. C. (2004). Building Detection by Dempster-Shafer Fusion of LIDAR Data and Multispectral Aerial Imagery. International Conference on Pattern Recognition, Cambridge, UK, 23-26 August. Los Alamitos, California: The Institute of Electrical and Electronics Engineers Computer Society. doi: 10.1109/ICPR.2004.1334203

  • Effect of Initial HMM Choices in Multiple Sequence Training for Gesture Recognition

    Liu, Nianjun, Davis, Richard I. A., Lovell, Brian C. and Kootsookos, Peter J. (2004). Effect of Initial HMM Choices in Multiple Sequence Training for Gesture Recognition. International Conference on Information Technology (ITCC 2004), Las Vegas, Nevada, U.S.A., 5-7 April, 2004. Los Alamitos, California: The Institute of Electrical and Electronics Engineers Computer Society. doi: 10.1109/ITCC.2004.1286531

  • Illumination and Expression Invariant Face Recognition With One Sample Image

    Chen, Shaokang and Lovell, Brian C. (2004). Illumination and Expression Invariant Face Recognition With One Sample Image. The Seventeenth International Conference on Pattern Recognition, Cambridge, UK, 23-26 August, 2004. Los Alamitos, California: The Institute of Electrical and Electronics Engineers Computer Society. doi: 10.1109/ICPR.2004.1334112

  • Model Structure Selection and Training Algorithms for a HMM Gesture Recognition System

    Liu, N., Lovell, B. C., Kootsookos, P. J. and Davis, R. I. (2004). Model Structure Selection and Training Algorithms for a HMM Gesture Recognition System. International Workshop in Frontiers of Handwriting Recognition, Tokyo, 26-29 October. Los Alamitos, California: The Institute of Electrical and Electronics Engineers Computer Society. doi: 10.1109/iwfhr.2004.68

  • Tensor Algebra: A Combinatorial Approach to the Projective Geometry of Figures

    McKinnon, David N. and Lovell, B. C. (2004). Tensor Algebra: A Combinatorial Approach to the Projective Geometry of Figures. IWCIA - Tenth International Workshop on Combinatorial Image Analysis, Auckland, New Zealand, 1-3 December, 2004. Berlin: Springer. doi: 10.1007/b103936

  • Understanding HMM Training For Video Gesture Recognition

    Liu, N., Lovell, B. C., Kootsookos, P. J. and Davis, R. I. A. (2004). Understanding HMM Training For Video Gesture Recognition. The Analog and Digital Techniques in Electrical Engineering, Chiang Mai, Thailand, 21-24 November, 2004. Piscataway, NJ: The Institute of Electrical and Electronics Engineers. doi: 10.1109/tencon.2004.1414483

  • 3D Reconstruction through Segmentation of Multi-View Image Sequences

    Leung, Carlos and Lovell, Brian C. (2003). 3D Reconstruction through Segmentation of Multi-View Image Sequences. Workshop on Digital Image Computing, Brisbane, 7 February, 2003. Brisbane: Australian Pattern Recognition Society.

  • A Closed Form Solution to the Reconstruction and Multi-View Constraints of the Degree d Apparent Contour

    McKinnon, David, Jones, Barry and Lovell, Brian C. (2003). A Closed Form Solution to the Reconstruction and Multi-View Constraints of the Degree d Apparent Contour. Workshop on Digital Image Computing, Brisbane, 7 February, 2003.

  • A closed form solution to the reconstruction and multi-view constraints of the degree d apparant contour

    McKinnon, D. N. R., Jones, B. D. and Lovell, B. C. (2003). A closed form solution to the reconstruction and multi-view constraints of the degree d apparant contour. The 2003 APRS Workshop on Digital Image Computing, Brisbane, 7 February, 2003. Brisbane: Australian Pattern Recognition Society.

  • Academic Performance of International Students in Electrical Engineering at the University of Queensland

    Lovell, Brian C. (2003). Academic Performance of International Students in Electrical Engineering at the University of Queensland. Australian Association for Engineering Education Conference, Melbourne, 29 September - 4 October. Melbourne: Australian Association for Engineering Education.

  • Algebraic Curve Fitting Support Vector Machines

    Walder, Christian, Lovell, Brian C. and Kootsookos, Peter J. (2003). Algebraic Curve Fitting Support Vector Machines. Digital Image Computing Techniques and Applications 2003, Sydney, December, 2003. Sydney: CSIRO.

  • Autonomous Direct 3D Segmentation of Articular Knee Cartilage

    Hinrichs, Enrico, Appleton, Ben, Lovell, Brian C. and Galloway, Graham John (2003). Autonomous Direct 3D Segmentation of Articular Knee Cartilage. Australian and New Zealand Intelligent Information Systems, Sydney, Australia, 10-12 December, 2003. Brisbane, Qld: Queensland University of Technology.

  • Autonomous Sports Training from Visual Cues

    Smith, Andrew W. B. and Lovell, Brian C. (2003). Autonomous Sports Training from Visual Cues. The 8th Australian and New Zealand Intelligent Information Systems Conference, Sydney, 10-12 December, 2003. Brisbane: Queensland University of Technology.

  • Color Optical Flow

    Andrews, Robert J. and Lovell, Brian C. (2003). Color Optical Flow. Workshop on Digital Image Computing, Brisbane, 7 February, 2003. Brisbane: Australian Pattern Recognition Society.

  • Evaluation of HMM training algorithms for letter hand gesture recognition

    Liu, Nianjun, Lovell, Brian C. and Kootsookos, Peter J. (2003). Evaluation of HMM training algorithms for letter hand gesture recognition. IEEE International Symposium on Signal Processing and Information Technology, Darmstadt, Germany, 14-17 December. Darmstadt, Germany: The Institute of Electrical and Electronics Engineers. doi: 10.1109/ISSPIT.2003.1341204

  • Face Recognition with One Sample Image per Class

    Chen, Shaokang and Lovell, Brian C. (2003). Face Recognition with One Sample Image per Class. Australian and New Zealand Intelligent Information Systems, Sydney, 10-12 December. Brisbane: Queensland University of Technology.

  • Gesture Classification Using Hidden Markov Models and Viterbi Path Counting

    Liu, Nianjun and Lovell, Brian C. (2003). Gesture Classification Using Hidden Markov Models and Viterbi Path Counting. The Seventh Biennial Australian Pattern Recognition Society Conference, Sydney, 10-12 December. Sydney: CSIRO.

  • Hidden Markov Models for Spatio-Temporal Pattern Recognition and Image Segmentation

    Lovell, Brian C. (2003). Hidden Markov Models for Spatio-Temporal Pattern Recognition and Image Segmentation. International Conference on Advances in Pattern Recognition, Calcutta, 10-13 December. Kolkata: Allied Publishers.

  • Improved Ensemble Training for Hidden Markov Models using Random Relative Node Permutations

    Davis, Richard I. A. and Lovell, Brian C. (2003). Improved Ensemble Training for Hidden Markov Models using Random Relative Node Permutations. The 2003 APRS Workshop on Digital Image Computing, Brisbane, 7 February, 2003. Brisbane: Australian Pattern Recognition Society.

  • Kernel Based Algebraic Curve Fitting

    Walder, Christian J. and Lovell, Brian C. (2003). Kernel Based Algebraic Curve Fitting. International Conference on Advances in Pattern Recogntion, Calcutta, 10-13 December. Kolkata: Allied Publishers.

  • OFCat: An Extensible GUI-Driven Optical Flow Comparison Tool

    Andrews, Robert J. and Lovell, Brian C. (2003). OFCat: An Extensible GUI-Driven Optical Flow Comparison Tool. The 2003 APRS Workshop on Digital Image Computing, Brisbane, 7 February, 2003. Brisbane: Australian Pattern Recognition Society.

  • Polytopes, Feasible Regions and Occlusions in the n-view Reconstruction Problem

    McKinnon, David, Jones, Barry and Lovell, Brian C. (2003). Polytopes, Feasible Regions and Occlusions in the n-view Reconstruction Problem. Workshop on Digital Image Computing, Brisbane, 7 February, 2003. Brisbane, Australia: Australian Pattern Recognition Society.

  • Towards Closed Form Solutions to the Multiview Constraints of Curves and Surfaces

    McKinnon, David N. and Lovell, Brian C. (2003). Towards Closed Form Solutions to the Multiview Constraints of Curves and Surfaces. The Seventh Biennial Australian Pattern Recognition Society Conference, Sydney, 10- 12 December. Sydney: CSIRO.

  • Towards a Maximum Entropy Method for Estimating HMM Parameters

    Walder, Christian J., Kootsookos, Peter J. and Lovell, Brian C. (2003). Towards a Maximum Entropy Method for Estimating HMM Parameters. Workshop on Digital Image Computing, Brisbane, February 7, 2003. Brisbane: Australian Pattern Recognition Society.

  • Face Recognition with APCA in Variant Illuminations

    Chen, S., Lovell, B. C. and Sun, S. (2002). Face Recognition with APCA in Variant Illuminations. Fourth Australasian Workshop on Signal Processing and Applications 2002, Brisbane, 17-18 December, 2002. Brisbane: Queensland University of Technology.

  • Face and Object Recognition and Detection Using Colour Vector Quantisation

    Walder, C. J. and Lovell, B. C. (2002). Face and Object Recognition and Detection Using Colour Vector Quantisation. Fourth Australasian Workshop on Signal Processing and Applications 2002, Brisbane, 17-18 December, 2002. Brisbane: Queensland University of Technology.

  • General Purpose Real-Time Object Tracking using Hausdorff Transforms

    Vignon, D., Lovell, Brian C. and Andrews, Robert J. (2002). General Purpose Real-Time Object Tracking using Hausdorff Transforms. 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Annency, France, 1-5 July, 2002. France: ESIA.

  • Improved Classification Using Hidden Markov Averaging From Multiple Observation Sequences

    Davis, R. I. A., Walder, C. J. and Lovell, Brian C. (2002). Improved Classification Using Hidden Markov Averaging From Multiple Observation Sequences. Fourth Australasian Workshop on Signal Processing and Applications 2002, Brisbane, 17-18 December, 2002. Brisbane: Queensland University of Technology.

  • Improved estimation of hidden Markov model parameters from multiple observation sequences

    Davis, Richard I. A., Lovell, Brian C. and Caelli, Terry (2002). Improved estimation of hidden Markov model parameters from multiple observation sequences. International Conference on Pattern Recognition, Quebec City, Canada, 11-15 August, 2002. The Institute of Electrical and Electronics Engineers. doi: 10.1109/icpr.2002.1048264

  • Low-Cost Real-Time Gesture Recognition

    Lovell, Brian C. and Heckenberg, Daniel (2002). Low-Cost Real-Time Gesture Recognition. ACCV2002, 22-25 January, 2002.

  • Low-cost real-time gesture recognition

    Lovell, B. C. and Heckenberg, D. R. (2002). Low-cost real-time gesture recognition. Digital Image Computing Techniques and Applications, Melbourne, 21-22 January, 2002. Melbourne: APRS.

  • Portable VXL system for computing structure from motion

    Mckinnon, D., Kubik, K. K. T. and Lovell, B. C. (2002). Portable VXL system for computing structure from motion. Digital Image Computing Techniques and Applications, Melbourne, 21-22 January, 2002. Melbourne: APRS.

  • Real-time Hausdorff-based tracking

    Vignon, D. and Lovell, B. C. (2002). Real-time Hausdorff-based tracking. Digital Image Computing Techniques and Applications, Melbourne, 21-22 January, 2002. Melbourne: APRS.

  • Real-time two hands tracking system

    Liu, N. and Lovell, B. C. (2002). Real-time two hands tracking system. The 2002 International Technical Conference on Circuits, Systems, Computers and Communications, Phuket, Thailand, 16-19 July, 2002. Thonburi, Thailand: King Mongkut's University of Technology.

  • MMX-Accelerated Real-Time Hand Tracking System

    Liu, Nianjun and Lovell, Brian C. (2001). MMX-Accelerated Real-Time Hand Tracking System. IVCNZ 2001, Dunedin, New Zealand, 26-28 November, 2001.

  • MMX-accelerated real-time hand tracking system

    Liu, N. and Lovell, B. C. (2001). MMX-accelerated real-time hand tracking system. Image and Vision Computing 2001, Dunedin, New Zealand, 26-28 November, 2001. Dunedin, New Zealand: Wickliffe Limited.

  • Method for Accurate Unsupervised Cell Nucleus Segmentation

    Bamford, Pascal and Lovell, Brian C. (2001). Method for Accurate Unsupervised Cell Nucleus Segmentation. IEEE Engineering in Medicine and Biology, Istanbul, Turkey, 25-28 October, 2001. Piscataway, New Jersey: IEEE.

  • Real-Time MMX-Accelerated Image Stabilization System

    Chen, Shaokang and Lovell, Brian C. (2001). Real-Time MMX-Accelerated Image Stabilization System. IVCNZ2001, Dunedin, New Zealand, 26-28 November, 2001.

  • Real-time MMX-accelerated image stabilization system

    Chen, S. and Lovell, B. C. (2001). Real-time MMX-accelerated image stabilization system. Image and Vision Computing 2001, Dunedin, New Zealand, 26-28 November, 2001. Dunedin, New Zealand: Wickliffe Limited.

  • MIME: A Gesture-Driven Computer Interface

    Heckenberg, D. and Lovell, Brian C. (2000). MIME: A Gesture-Driven Computer Interface. Visual Communications and Image Processing, SPIE, V 4067, Perth, Australia, 20-23 June, 2000. doi: 10.1117/12.386641

  • Real-Time Face Recognition Using Eigenfaces

    Cendrillon, Raphael and Lovell, Brian C. (2000). Real-Time Face Recognition Using Eigenfaces. Visual Communications and Image Processing, SPIE, V 4067, Perth, 20-23 June, 2000. doi: 10.1117/12.386642

  • A methodology for quality control in cell nucleus segmentation

    Bamford, Pascal and Lovell, Brian C. (1999). A methodology for quality control in cell nucleus segmentation. Digital Image Computing: Techniques and Applications, Perth, Australia, 7th - 8th December, 1999. Perth: Australian Pattern Recognition Society.

  • On the Open-Ended Classifier Problem in the Context of Human Face Recognition and Tracking in Cluttered Visual Environments

    Lovell, Brian C., Kootsookos, Peter J. and Longstaff, Dennis (1999). On the Open-Ended Classifier Problem in the Context of Human Face Recognition and Tracking in Cluttered Visual Environments. Digital Image Computing Techniques and Applications, Perth, 7th - 8th December, 1999. Perth: Australian Pattern Recognition Society.

  • Progress in the robust automated segmentation of real cell images

    Bamford, P. C., Jackway, P. T. and Lovell, Brian (1999). Progress in the robust automated segmentation of real cell images. New Approaches in Medical Image Analysis, Ballarat, Australia, 31 July 1998. Bellingham: SPIE - The Int. Society for Optical Engineering. doi: 10.1117/12.351626

  • Bayesian Analysis of Cell Nucleus Segmentation by a Viterbi Search Based Active Contour

    Bamford, Pascal and Lovell, Brian C. (1998). Bayesian Analysis of Cell Nucleus Segmentation by a Viterbi Search Based Active Contour. International Conference on Pattern Recognition, Brisbane, Australia, August 16 - 20, 1998. doi: 10.1109/icpr.1998.711098

  • Improving the Robustness of Cell Nucleus Segmentation

    Bamford, Pascal and Lovell, Brian C. (1998). Improving the Robustness of Cell Nucleus Segmentation. British Machine Vision Conference, Southampton, UK, September 14 - 17, 1998.

  • Texture Classification Using Nonparametric Random Fields

    Paget, R., Lovell, Brian C. and Longstaff, I. D. (1997). Texture Classification Using Nonparametric Random Fields. 13th International Conference on Signal Processing, Santorini, Greece, July 2-4, 1997. doi: 10.1109/icdsp.1997.627969

  • Two-stage scene segmentation scheme for the automatic collection of cervical cell images

    Bamford Pascal and Lovell Brian (1997). Two-stage scene segmentation scheme for the automatic collection of cervical cell images. Proceedings of the 1997 IEEE TENCON Conference. Part 1 (of 2), Brisbane, Australia, December 2, 1997-December 4, 1997. doi: 10.1109/tencon.1997.648513

  • A Water Immersion Algorithm for Cytological Image Segmentation

    Bamford, Pascal and Lovell, Brian C. (1996). A Water Immersion Algorithm for Cytological Image Segmentation. APRS Image Segmentation Workshop, Sydney, Australia, 13 December, 1996.

  • Classification In Scale-Space: Applications To Texture

    Bradley, Andrew P., Jackway, Paul and Lovell, Brian C. (1995). Classification In Scale-Space: Applications To Texture. XIVth International Conference on Information Processing in Medical Imaging (IPMI), Ile de Berder, France,

  • Classification in scale-space: Applications to texture analysis

    Bradley, AP, Jackway, PT and Lovell, BC (1995). Classification in scale-space: Applications to texture analysis. 14th International Conference on Information Processing in Medical Imaging, Ile De Berder France, Jun, 1995. DORDRECHT: KLUWER ACADEMIC PUBL.

  • Cost-Sensitive Decision Tree Pruning: Use of the ROC Curve

    Bradley, Andrew P. and Lovell, Brian C. (1995). Cost-Sensitive Decision Tree Pruning: Use of the ROC Curve. Eighth Australian Joint Conference on Artificial Intelligence, Canberra, Australia, November, 1995.

  • Scale-Space Texture Analysis

    Bradley, Andrew P., Lovell, Brian C. and Jackway, Paul T. (1995). Scale-Space Texture Analysis. Digital Image Computing: Techniques and Applications (DICTA '95), Brisbane, December, 1995.

  • A Multi-Resolution Algorithm for Cytological Image Segmentation

    Jeacocke, Mark B. and Lovell, Brian C. (1994). A Multi-Resolution Algorithm for Cytological Image Segmentation. Australian and New Zealand Conference on Intelligent Information Systems, Brisbane, Australia, 1994. doi: 10.1109/anziis.1994.396981

  • Classification Of Cervical Cell Nuclei Using Morphological Segmentation And Texture Feature Extraction

    Walker, Ross F., Jackway, Paul, Lovell, Brian C. and Longstaff, I. D. (1994). Classification Of Cervical Cell Nuclei Using Morphological Segmentation And Texture Feature Extraction. Australian and New Zealand Conference on Intelligent Information Systems, Brisbane, Australia, 1994. doi: 10.1109/anziis.1994.396977

  • Inductive Learning using Multiscale Classification

    Bradley, Andrew P. and Lovell, Brian C. (1994). Inductive Learning using Multiscale Classification. Fifth Australian Conference on Neural Networks (ACNN '94), Brisbane, Australia, February, 1994.

  • Modelling and Classification of Shapes in Two-Dimensions Using Vector Quantization

    Lee, S. and Lovell, Brian C. (1994). Modelling and Classification of Shapes in Two-Dimensions Using Vector Quantization. IEEE International Conference on ASSP, Adelaide Australia, 1994. doi: 10.1109/icassp.1994.389428

  • On the Methodology for Comparing Learning Algorithms: A Case Study

    Bradley, Andrew P., Lovell, Brian C., Ray, Michael and Hawson, Geoffrey (1994). On the Methodology for Comparing Learning Algorithms: A Case Study. Australian and New Zealand Conference on Intelligent Information Systems (ANZIIS'94), Brisbane, Australia, November, 1994. doi: 10.1109/anziis.1994.396954

  • Mutiprocessor Adaptation of a Texture Segmentation Scheme for Satellite Radar Images

    Paget, R., Lovell, Brian C. and Longstaff, I. D. (1993). Mutiprocessor Adaptation of a Texture Segmentation Scheme for Satellite Radar Images. Digital Image Computing: Techniques and Applications, Sydney, Australia, December, 1993.

  • Speech Enhancement for Robust Speaker Verification

    Kootsookos, Peter J., Tsoi, A. C. and Lovell, Brian C. (1992). Speech Enhancement for Robust Speaker Verification. Speech, Science and Technology conference, Brisbane, December.

  • The Circular Nature Of Discrete-Time Frequency Estimates

    Lovell, Brian C., Kootsookos, Peter J. and Williamson, R. C. (1991). The Circular Nature Of Discrete-Time Frequency Estimates. IEEE International Conference on ASSP, Toronto, May, 1991. Publ by IEEE. doi: 10.1109/icassp.1991.150176

  • Efficient Frequency Estimation and Time-Frequency Representations

    Lovell, Brian C., Kootsookos, Peter J. and Williamson, Robert C. (1990). Efficient Frequency Estimation and Time-Frequency Representations. International Symposium on Signal Processing and Its Applications 1990, Gold Coast, Australia, August.

  • Time-Frequency Signal Analysis and Instantaneous Frequency Estimation: Methodology, Relationships and Implementation

    Boashash, Boualem, Lovell, Brian C. and Kootsookos, Peter J. (1989). Time-Frequency Signal Analysis and Instantaneous Frequency Estimation: Methodology, Relationships and Implementation. International Symposium on Circuits and Systems, 1989. doi: 10.1109/iscas.1989.100579

  • Segmentation of Non-Stationary Signals with Applications

    Boashash, B. and Lovell, Brian C. (1988). Segmentation of Non-Stationary Signals with Applications. IEEE International Conference on ASSP, New York, 1988. doi: 10.1109/icassp.1988.197203

Edited Outputs

  • Digital Image Computing: Techniques and Applications (DICTA 2005)

    B. C. Lovell, A. J. Maeder, T. Caelli and S. Ourselin eds. (2005). Digital Image Computing: Techniques and Applications (DICTA 2005). Digital Imaging Computing: Techniques and Applications (DICTA 2005), Cairns, QLD Australia, 6-8 December 2005. Los Alamitos, CA United States: IEEE Computer Society.

  • Digital image computing: Techniques and applications

    Lovell, Brian C., Maeder, Anthony J., Caelli, Terry and Ourselin, Sebastien eds. (2005). Digital image computing: Techniques and applications. DICTA 2005: Digital Image Computing Techniques and Applications, Cairns, Qld., Australia, 6-8 December, 2005. Los Alamitos, Calif.: IEEE.

  • Proceedings of the APRS Workshop on Digital Image Computing

    Brian C. Lovell and Anthony J. Maeder eds. (2005). Proceedings of the APRS Workshop on Digital Image Computing. APRS Workshop on Digital Image Computing, Griffith University, Southbank, Brisbane, Australia, 21 February, 2005. St Lucia, Queensland: The University of Queensland.

  • Proceedings of the 2003 APRS Workshop on Digital Image Computing

    Lovell, Brian C. and Maeder, Anthony J. eds. (2003). Proceedings of the 2003 APRS Workshop on Digital Image Computing. Workshop on Digital Image Computing, Brisbane, February 7, 2003.

  • Proceedings of the 2003 APRS Workshop on Digital Image Computing

    Brian C. Lovell and Anthony, J. Maeder eds. (2003). Proceedings of the 2003 APRS Workshop on Digital Image Computing. The 2003 APRS Workshop on Digital Image Computing (WDIC 2003), Brisbane, Queensland, 7 February, 2003. Brisbane, Queensland: The Australian Pattern Recognition Society.

  • Proceedings of the Eighth Australian and New Zealand Intelligent Information Systems Conference

    Brian C. Lovell, Duncan A. Campbell, Clinton B. Fookes and Anthony J. Maeder eds. (2003). Proceedings of the Eighth Australian and New Zealand Intelligent Information Systems Conference. Proceedings of the Eighth Australian and New Zealand Intelligent Information Systems Conference (ANZIIS 2003), Sydney, Australia, 10-12 December, 2003. Brisbane, QLD: The Australian Pattern Recognition Society.

Other Outputs

Grants (Administered at UQ)

PhD and MPhil Supervision

Current Supervision

  • Modelling Efficient and Robust Solutions for Microbiology Image Analysis Using Deep Learning

    Doctor Philosophy — Principal Advisor

  • Data Augmentation through Image Synthesis and Editing in Multi-domains via Disentangled Latent Space

    Doctor Philosophy — Principal Advisor

  • The role of duality in machine learning and computer vision.

    Doctor Philosophy — Principal Advisor

    Other advisors:

  • Medical Image Segmentation with Limited Annotated Data

    Doctor Philosophy — Principal Advisor

    Other advisors:

  • Enhancing Building Fire Safety by Utilising Machine Learning Techniques

    Doctor Philosophy — Associate Advisor

    Other advisors:

  • Generating data-driven continuous optimization problems for benchmarking

    Doctor Philosophy — Associate Advisor

    Other advisors:

  • Modelling cloud movement to generate short term solar irradiance predictions and subsequent expected PV power production

    Doctor Philosophy — Associate Advisor

    Other advisors:

Completed Supervision

Possible Research Projects

Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.

  • Detecting and Classifying Neurofibromas using Deep Learning

    Neurofibromatosis type 1 (NF1) is one of the most common single-gene inherited disorders globally, with an incidence of 1/2500 individuals. While several phenotypes are associated with the disorder, the most common manifestation is cutaneous neurofibroma. The majority of adults develop these distressing cutaneous tumours (cNF), which increase in severity with age. Adult patients report cosmetic disfigurement due to cNF as the greatest burden of living with NF1. There is no way to predict tumour severity which can range from <100 to thousands. Youth and families experience reduced quality of life due to concerns about this uncertain future. We don’t yet understand why this condition is so variable or have any effective medical treatments. In the proposed research, we will assemble a consortium of internationally recognised experts in NF1 with access and capacity to recruit and phenotype patients to drive the largest genome-wide association and epigenome-wide association studies of the modifier gene networks driving the cutaneous phenotypic variance in NF1. We will then use individualised pharmacological annotation of these networks to identify precision treatment options to mitigate the most distressing and life quality damaging aspects of this devastating illness.

  • Classifying Gram Stain Images Using Transformers and Deep Learning

    Microscopic diagnosis of Gram stain smears is one of the most time and labor intensive tasks in the clinical setting. With the recent development of automated digital pathology scanners, it is now possible to economically obtain high-resolution Gram stain whole slide images for routine diagnosis. This finally opens the doorway to automated identification of bacteria types from digital images in a clinical setting. However, Gram stain whole slide images comprise billions of pixels and suffer from high morphological heterogeneity as well as from many different types of artifacts. Identifying multiple types of tiny bacteria with various densities from an extremely large whole slide image is incredibly challenging. To this end, we propose an end-to-end framework with a novel loss function that tackles the patch aggregation while considering the correlation of different labels in this multi-label scenario. Our framework first effectively integrates the relations among multiple patch features, and then utilizes a class aggregator to generate a robust slide-level feature representation under multi-label setting. Furthermore, we propose a novel loss function integrating two regularization terms: 1) a negative regulator that reduces the confusion between bacteria and negative samples without any bacteria, and 2) an adversarial loss that alleviates the impact of background specification among various tissue samples. We show that the proposed method achieves superior performance compared to several state-of-the-art methods.

  • Incremental Learning for AI

    Incremental learning requires a model to continually learn new tasks from streaming data. However, traditional fine-tuning of a well-trained deep neural network on a new task will dramatically degrade performance on the old task — a problem known as catastrophic forgetting. We address this issue in the context of anchor-free object detection, which is a new trend in computer vision as it is simple, fast, and flexible. Simply adapting current incremental learning strategies fails on these anchor-free detectors due to lack of consideration of their specific model structures. To deal with the challenges of incremental learning on anchor-free object detectors, we propose a novel incremental learning paradigm called Selective and Inter-related Distillation (SID). In addition, a novel evaluation metric is proposed to better assess the performance of detectors under incremental learning conditions. By selective distilling at the proper locations and further transferring additional instance relation knowledge, our method demonstrates significant advantages on the benchmark datasets PASCAL VOC and COCO.

  • Text to Face Synthesis using Stable Diffusion for Biometrics Research

    Text-to-Face (TTF) synthesis is a challenging task with great potential for diverse computer vision applications. Compared to Text-to-Image (TTI) synthesis tasks, the textual description of faces can be much more complicated and detailed due to the variety of facial attributes and the parsing of high dimensional abstract natural language. We propose a text-to-face model that should not only produce images in high resolution (10241024) and text-to-image consistency, but also output multiple faces to cover a wide range of unspecified facial features in a natural way. By fine-tuning the multi-label classifier and image encoder, our model obtains the vectors and image embeddings which are used to manipulate the noise vector sampled from the normal distribution. Afterwards, the manipulated noise vector is fed into a pre-trained high-resolution image generator to produce a set of faces with desired facial attributes. We refer to our model as TTF-HD. Experimental results show that TTF-HD generates high-quality faces and achieves state-of-the-art performance.

This staff member is a UQ Expert for media in the following fields:

Image processing, Information technology, Pattern recognition - digital imaging, Computer vision, Face-recognition technology, Identification technology, National security surveillance, Border control, Face Recognition, Wearable Technologies, Biometrics, Pattern Recognition, Computer modelling, Neural networks - artificial, Networks - neural, Artificial Intelligence, Deep Learning, Signal Processing

They are happy to lend their expertise to your articles or broadcasts and share their research discoveries and insights with the community via media channels.

For additional assistance with story ideas, general advice and information or help with seeking further experts, please email the UQ Media Team or telephone (07) 3365 1120.

Links

ORCID: 0000-0001-6722-1754

Scopus ID:

Google Scholar ID: gXiGxcMAAAAJ

Personal Profile: Link

Unit Links
Other Media
Social Media

Related Resources

Similar Staff
Researchers in Associated Units
Fields of Research