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Notice

AISB miscellaneous Bulletin Item

Extended Call for chapter proposals: Machine Learning for Human Motion Analysis: Theory and Practice

http://www.cs.mu.oz.au/~lwwang/index_files/book_behavior.html

Upon many requests, as well as for the purpose of matching the ECCV08 MLVMA08 (http://www.cs.mu.oz.au/~lwwang/index_files/workshop08.html) schedule, the deadline of chapter proposals submission has been extended to 15 August.

http://www.cs.mu.oz.au/~lwwang/index_files/book_behavior.html

CALL FOR CHAPTER PROPOSALS
Proposal Submission Deadline: August 15, 2008
Machine Learning for Human Motion Analysis: Theory and Practice
A Book Edited by Dr. Liang Wang, The University of Melbourne, Australia
Dr. Li Cheng, National ICT Australia
                  Dr. Guoying Zhao, University of Oulu, Finland
 
Introduction
Vision-based motion analysis aims to detect, track and identify objects, and more generally, to understand their behaviors, from video sequences. With the ubiquitous presence of video data and the increasing importance in a wide range of real-world applications such as visual surveillance, human-machine interface and sport event interpretation, it is becoming increasingly demanding to automatically analyze and understand object motions from large amounts of video footage. 
    Statistical machine learning algorithms have been recently successfully applied to address challenging problems involved in this area. Novel statistical learning technologies have a strong potential to contribute to the development of robust yet flexible vision systems. The process of improving the performance of vision systems has also brought new challenges to the field of machine learning. Solving the problems involved in object motion analysis will lead to the development of new machine learning algorithms. In return, new machine learning algorithms are able to address more realistic problems in object motion analysis and understanding. 
 
Objective of the Book
This edited book will highlight the development of robust and effective vision-based motion understanding systems from a machine learning perspective. Major contributions of this book are as follows:  (1) It will provide new researchers with a comprehensive review of the recent development in this field, and present a variety of study cases where the state-of-the-art learning algorithms are devised to address specific tasks in human motion understanding; (2) It will give the readers a clear picture of the most active research forefronts and discussions of challenges and future directions, which different levels of researchers might find to be useful for guiding their future research. (3) It will draw great strength from the research communities of human motion understanding and machine learning and demonstrates the benefits from the interaction and collaboration of both fields.
 
Target Audience
The targeted audiences are mainly researchers, engineers as well as graduate students in the areas of computer vision and machine learning. The book is also intend to be accessible to a broader audience including practicing professionals working with specific vision applications such as surveillance, sport event analysis, healthcare, video conferencing, and motion video indexing and retrieval.
 
Recommended topics include, but are not limited to, the following:
Machine Learning Theories
o    Supervised/unsupervised/semi-supervised learning
o    Generative and discriminative approaches
o    Probabilistic graphical models and exponential families
o    Large-margin method with structured output
o    Manifold learning
o    Kernel machines
o    Online and incremental learning
Vision-based Motion Analysis and Understanding
o    Motion segmentation and object recognition
o    Human detection and tracking
o    Motion feature extraction and representation
o    Motion analysis and understanding
o    Activity analysis and unusual event detection
Machine Learning in Motion Analysis and Understanding
o    Online and incremental learning to human detection and tracking
o    Manifold learning for pose estimation
o    Manifold learning for motion tracking
o    Large-margin method based action classification
o    Boosting algorithms for motion feature selection and classification
o    Generative or discriminative approaches for action modeling
o    Hybrid graphical models for action recognition
o    Clustering for categorization of motion patterns
o    Learning to segment and recognize actions
o    Learning to discover anomaly activities and events
o    Learning to analyze motions using stereo and camera array
o    Learning to analyze activities using multi-modality sensors
 
Submission Procedure
Researchers and practitioners are invited to submit on or before August 15, 2008, a 2-3 page chapter proposal clearly explaining the mission and concerns of the proposed chapter, together with a tentative title and chapter organization. Proposals will be accepted based on pertinence criteria and topic balancing needs. Authors of accepted proposals will be notified by August 31, 2008 about the status of their proposals and sent chapter guidelines. Full chapters are expected to be submitted no later than November 30, 2008. All submitted chapters will be reviewed on a double-blind review basis. The book is scheduled to be published by IGI Global (formerly Idea Group Inc.), www.igi-global.com, publisher of the IGI Publishing (formerly Idea Group Publishing), Information Science Publishing, IRM Press, CyberTech Publishing, Information Science Reference (formerly Idea Group Reference), and Medical Information Science Reference imprints.
 
Inquiries and submissions can be forwarded electronically (Word document) or by mail to:
 
Dr. Liang Wang
Department of Computer Science & Software Engineering
The University of Melbourne, Parkville, Vic 3010, Melbourne, Australia
Tel.: +61 3 8344 1364  Fax: +61 3 9348 1184
Email: lwwang@csse.unimelb.edu.au
 
Dr. Li Cheng
Canberra Research Lab
National ICT Australia, Locked Bag 8001, Canberra ACT 2612
Mobile: +61 432 572 310
Email: licheng.nicta@gmail.com
 
Dr. Guoying Zhao
Department of Electrical and Information Engineering
P.O.Box 4500 FI-90014 University of Oulu, Finland
Phone: +358 8 553 7564   Fax: +358 8 553 2612
Email: gyzhao@ee.oulu.fi