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Automated recognition of mouse behaviours
时间:2018-07-04 14:49    点击:   所属单位:人工智能学院
讲座名称 Automated recognition of mouse behaviours
讲座时间 2018-07-06 09:30:00
讲座地点 主楼II-402
讲座人 Huiyu Zhou 教授
讲座人介绍 Dr. Huiyu Zhou received a Bachelor of Engineering degree in Radio Technology from Huazhong University of Science and Technology of China, and a Master of Science degree in Biomedical Engineering from University of Dundee of United Kingdom, respectively. He was awarded a Doctor of Philosophy degree in Computer Vision from Heriot-Watt University, Edinburgh, United Kingdom. Dr. Zhou currently is a Reader at Department of Informatics, University of Leicester, United Kingdom. He has published over 150 peer-reviewed papers in the field. He was the recipient of "Computer Vision and Image Understanding 2012 Most Cited Paper Award", “International Conference on Pattern Recognition Applications and Methods (ICPRAM) 2016 Best Paper Award” and was nominated for “ICPRAM 2017 Best Student Paper Award” and "Medical & Biological Engineering & Computing 2006 Nightingale Prize". Four of his papers recently published by Elsevier were ranked as the ScienceDirect Top 25 Articles. Dr. Zhou serves as the Editor-in-Chief of Recent Advances in Electrical & Electronic Engineering and Associate Editor of "IEEE Transaction on Human-Machine Systems", and is on the Editorial Boards of five refereed journals. He is one of the Technical Committee of “Information Assurance & Intelligent Multimedia-Mobile Communication in IEEE SMC Society”, “Robotics Task Force” and “Biometrics Task Force” of the Intelligent Systems Applications Technical Committee, IEEE Computational Intelligence Society. He has given over 50 invited talks at international conferences, industry and universities, and has served as a chair for 30 international conferences and workshops. His research work has been or is being supported by UK EPSRC, MRC, EU, Royal Society, Leverhulme Trust, Puffin Trust, Invest NI and industry.
讲座内容 Automated recognition of mouse behaviours is crucial in studying psychiatric and neurologic diseases, e.g. Parkinson’s disease. To achieve this objective, it is very important to analyse temporal dynamics of mouse behaviours. In this paper, we develop and implement a novel Hidden Markov Model (HMM) algorithm to describe the temporal characteristics of mouse behaviours. In particular, we here propose a hybrid deep learning architecture, where the first unsupervised layer relies on a new spatial-temporal segment Fisher Vector (SFV) encoding both visual and contextual features. Subsequent supervised layers based on our segment aggregated network (SAN) are trained to estimate the state dependent observation probabilities of the HMM. Finally, we evaluate our approach using JHuang’s and our own datasets, and the results show that our method outperforms other state-of-the-art approaches.
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