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Brendon Woodford

NZCDP(Stage III), BSc, PGCertTertT, PGDipSci, MSc, PhD (Otago)

Position
Senior Lecturer
Room
8.09, Commerce Building
Phone
+64 3 479 5432
Email
brendon.woodford@otago.ac.nz
Supervising
Feng Zhou
Co-supervising
Ahmad Shahi
Papers
2017 S1: INFO201, HEIN703
2017 S2: INFO202, INFO411, COMP111
2017 FY: INFO501, INFO580
Research group
Intelligent Computing and Networking

Dr Brendon Woodford lectures in the diverse areas of knowledge engineering, machine learning, information systems development, and health informatics.

He conducts research for the Knowledge, Intelligence and Web Informatics (KIWI) Laboratory in the areas of artificial neural networks, fuzzy systems, data visualisation, and image processing and recognition. Since 1998 the overall aim of this research is in the area of computational intelligence. The main objective of his work has been to create adaptive learning systems which generate new knowledge from data that they process and allow for improved decision making in difficult real-world domains.

In the past the application of this work has been improving upon existing machine learning techniques to support decision making and data mining primarily in the horticultural domain but the current focus in now in the health data analytics domain.

This research has contributed to 27 publications to date in both international journals and in some of the top international conferences. In 2008 he completed a PhD which primarily looked at the development and implementation of intelligent decision support systems for New Zealand’s horticulture industry.

Publications

Shahi, A., Deng, J. D., & Woodford, B. J. (2017). A streaming ensemble classifier with multi-class imbalance learning for activity recognition. Proceedings of the International Joint Conference on Neural Networks (IJCNN). (pp. 3983-3990). IEEE. doi: 10.1109/IJCNN.2017.7966358

Lin, H., Deng, J. D., & Woodford, B. J. (2016). Shot boundary detection using multi-instance incremental and decremental one-class support vector machine. In J. Bailey, L. Khan, T. Washio, G. Dobbie, J. Z. Huang & R. Wang (Eds.), Advances in knowledge discovery and data mining: Lecture Notes in Artificial Intelligence (Vol. 9651). (pp. 165-176). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-31753-3_14

Lin, H., Deng, J. D., Woodford, B. J., & Shahi, A. (2016). Online weighted clustering for real-time abnormal event detection in video surveillance. Proceedings of the Association for Computing Machinery (ACM) on Multimedia Conference. (pp. 536-540). New York, NY: ACM. doi: 10.1145/2964284.2967279

Shah, M., Deng, J. D., & Woodford, B. J. (2015). A Self-Adaptive CodeBook (SACB) model for real-time background subtraction. Image & Vision Computing, 38, 52-64. doi: 10.1016/j.imavis.2015.02.001

Shahi, A., Woodford, B. J., & Deng, J. D. (2015). Event classification using adaptive cluster-based ensemble learning of streaming sensor data. In B. Pfahringer & J. Renz (Eds.), Advances in artificial intelligence: Lecture notes in artificial intelligence (Vol. 9457). (pp. 505-516). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-26350-2_45

Chapter in Book - Research

Lin, H., Deng, J. D., & Woodford, B. J. (2016). Shot boundary detection using multi-instance incremental and decremental one-class support vector machine. In J. Bailey, L. Khan, T. Washio, G. Dobbie, J. Z. Huang & R. Wang (Eds.), Advances in knowledge discovery and data mining: Lecture Notes in Artificial Intelligence (Vol. 9651). (pp. 165-176). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-31753-3_14

Shah, M., Deng, J., & Woodford, B. (2013). Illumination invariant background model using mixture of Gaussians and SURF features. In J.-I. Park & J. Kim (Eds.), Computer vision: ACCV 2012 workshops: Lecture notes in computer science (Vol. 7728). (pp. 308-314). Berlin, Germany: Springer. doi: 10.1007/978-3-642-37410-4_27

Kasabov, N. K., Israel, S. A., & Woodford, B. J. (1999). Adaptive, evolving, hybrid connectionist systems for image pattern recognition. In S. Pal, A. Ghosh & M. Kundu (Eds.), Soft Computing for Image Processing. (pp. 318-336). Heidleberg, Germany: Springer Verlag.

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Journal - Research Article

Shah, M., Deng, J. D., & Woodford, B. J. (2015). A Self-Adaptive CodeBook (SACB) model for real-time background subtraction. Image & Vision Computing, 38, 52-64. doi: 10.1016/j.imavis.2015.02.001

Shah, M., Deng, J. D., & Woodford, B. J. (2014). Video background modeling: Recent approaches, issues and our proposed techniques. Machine Vision & Applications, 25(5), 1105-1119. doi: 10.1007/s00138-013-0552-7

Mann, S. L., Marshall, M. R., Woodford, B. J., Holt, A., & Williams, A. B. (2012). Predictive performance of Acute Physiological and Chronic Health Evaluation releases II to IV: A single New Zealand centre experience. Anaesthesia & Intensive Care, 40(3), 479-489.

Mann, S. L., Marshall, M. R., Holt, A., Woodford, B., & Williams, A. B. (2010). Illness severity scoring for Intensive Care at Middlemore Hospital, New Zealand: Past and future. New Zealand Medical Journal, 123(1316). Retrieved from http://journal.nzma.org.nz/journal/123-1316/4157/content.pdf

Woodford, B. J. (2008). Evolving neurocomputing systems for horticulture applications. Applied Soft Computing, 8, 564-578. doi: 10.1016/j.asoc.2006.05.006

Shaw, D., Woodford, B. J., & Benwell, G. L. (2007). Educating future IS professionals through real-world integration. International Journal of Teaching & Case Studies, 1(1/2), 66-83.

Kasabov, N. K., Israel, S. A., & Woodford, B. J. (2000). The application of hybrid evolving connectionist systems to image classification. International Journal of Advanced Computational Intelligence, 4(1), 57-65.

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Conference Contribution - Published proceedings: Full paper

Shahi, A., Deng, J. D., & Woodford, B. J. (2017). A streaming ensemble classifier with multi-class imbalance learning for activity recognition. Proceedings of the International Joint Conference on Neural Networks (IJCNN). (pp. 3983-3990). IEEE. doi: 10.1109/IJCNN.2017.7966358

Lin, H., Deng, J. D., Woodford, B. J., & Shahi, A. (2016). Online weighted clustering for real-time abnormal event detection in video surveillance. Proceedings of the Association for Computing Machinery (ACM) on Multimedia Conference. (pp. 536-540). New York, NY: ACM. doi: 10.1145/2964284.2967279

Shahi, A., Woodford, B. J., & Deng, J. D. (2015). Event classification using adaptive cluster-based ensemble learning of streaming sensor data. In B. Pfahringer & J. Renz (Eds.), Advances in artificial intelligence: Lecture notes in artificial intelligence (Vol. 9457). (pp. 505-516). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-26350-2_45

Lin, H., Deng, J. D., & Woodford, B. J. (2015). Anomaly detection in crowd scenes via online adaptive one-class support vector machines. Proceedings of the International Conference on Image Processing (ICIP). (pp. 2434-2438). IEEE. doi: 10.1109/icip.2015.7351239

Lin, H., Deng, J. D., & Woodford, B. J. (2014). Spatial-temporal pyramid matching for crowd scene analysis. In A. Rahman, J. Deng & J. Li (Eds.), Proceedings of the 2nd Workshop on Machine Learning for Sensory Data Analysis (MLSDA). (pp. 12-18). New York: ACM. doi: 10.1145/2689746.2689751

Shah, M., Deng, J. D., & Woodford, B. J. (2013). Improving mixture of Gaussians background model through adaptive learning and spatio-temporal voting. Proceedings of the International Conference on Image Processing (ICIP). (pp. 3436-3440). IEEE. doi: 10.1109/ICIP.2013.6738709

Shah, M., Deng, J. D., & Woodford, B. J. (2013). Growing neural gas video background model (GNG-BM). In S. Cranefield & A. Nayak (Eds.), Advances in artificial intelligence: Lecture notes in artificial intelligence (Vol. 8272). (pp. 135-147). Heidelberg, Germany: Springer. doi: 10.1007/978-3-319-03680-9_15

Lin, H., Deng, J. D., & Woodford, B. J. (2013). Event detection using quantized binary code and spatial-temporal locality preserving projections. In S. Cranefield & A. Nayak (Eds.), Advances in artificial intelligence: Lecture notes in artificial intelligence (Vol. 8272). (pp. 123-134). Heidelberg, Germany: Springer. doi: 10.1007/978-3-319-03680-9_14

Shah, M., Deng, J. D., & Woodford, B. J. (2012). Enhancing the Mixture of Gaussians background model with local matching and local adaptive learning. In B. McCane, S. Mills & J. D. Deng (Eds.), Proceedings of the 27th Image and Vision Computing New Zealand Conference (IVCNZ). (pp. 103-108). New York: ACM. [Full Paper]

Lin, H., Deng, J. D., & Woodford, B. J. (2012). Video manifold modelling: Finding the right parameter settings for anomaly detection. In B. McCane, S. Mills & J. D. Deng (Eds.), Proceedings of the 27th Image and Vision Computing New Zealand Conference (IVCNZ). (pp. 168-173). New York: ACM. [Full Paper]

Shah, M., Deng, J., & Woodford, B. (2011). Enhanced codebook model for real-time background subtraction. In B.-L. Lu, L. Zhang & J. Kwok (Eds.), Neural information processing: Lecture notes in computer science (Vol. 7064). (pp. 449-458). Berlin, Germany: Springer. doi: 10.1007/978-3-642-24965-5_51

Woodford, B. J. (2010). Automatic optimization of pruning in evolving fuzzy neural networks using an entropy measure. Proceedings of the IEEE World Congress on Computational Intelligence (WCCI). (pp. 1053-1059). IEEE. [Full Paper]

Shah, M., Deng, J., & Woodford, B. J. (2010). Localized adaptive learning of Mixture of Gaussians models for background extraction. Proceedings of the 25th International Conference of Image and Vision Computing New Zealand (IVCNZ). doi: 10.1109/IVCNZ.2010.6148870

Woodford, B. J. (2008). Rule extraction from spatial data using a entropy-based evolving fuzzy neural network. In P. A. Whigham, I. Drecki & A. Moore (Eds.), Proceedings of the 20th Annual Colloquium of the Spatial Information Research Centre in conjunction with the New Zealand Cartographic Society Inc and GeoComp. (pp. 55-66). Dunedin, New Zealand: Spatial Information Research Centre and the New Zealand Cartographic Society Inc. [Full Paper]

Woodford, B. J. (2005). Rule extraction from spatial data using local learning techniques. In P. A. Whigham (Ed.), Proceedings of the 17th Annual Colloquium of the Spatial Information Research Centre. (pp. 125-130). Dunedin, New Zealand: University of Otago. [Full Paper]

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