Title:	A Family of Stochastic Dynamical Models for Video with Applications to Computer Vision
Date:	April 15, 2009 (Wednesday)
Time:	2:00 p.m. - 3:00 p.m.
Venue:	Room 121, 1/F, Ho Sin-hang Engineering Building, The Chinese University of Hong Kong, Shatin, N.T.
Speaker:	Dr. Antoni Chan Statistical Visual Computing Laboratory University of California, San Diego (UCSD) USA

ABSTRACT:

One family of visual processes that has relevance for various applications of computer vision is that of, what could be loosely described as, visual processes composed of ensembles of particles subject to stochastic motion. The particles can be microscopic (e.g plumes of smoke), macroscopic (e.g. leaves blowing in the wind), or even objects (e.g. a human crowd or a traffic jam). The applications range from remote monitoring for the prevention of natural disasters (e.g. forest fires), to background subtraction in challenging environments (e.g. outdoor scenes with moving trees in the background), and to surveillance (e.g. traffic monitoring). Despite their practical significance, the visual processes in this family still pose tremendous challenges for computer vision. In particular, the stochastic nature of the motion fields tends to be highly challenging for traditional motion representations such as optical flow, parametric motion models, and object tracking. Recent efforts have advanced towards modeling video motion probabilistically, by viewing video sequences as "dynamic textures" or, more precisely, samples from a generative, stochastic, texture model defined over space and time. Despite its successes in applications such as video synthesis, motion segmentation, and video classification, the dynamic texture model has several major limitations, such as an inability to account for visual processes consisting of multiple co-occurring textures (e.g. smoke rising from a fire), and an inability to model complex motion (e.g. panning camera motion).

In this talk, I propose a family of dynamical models for video that address the limitations of the dynamic texture, and apply these new models to challenging computer vision problems. In particular, we introduce two multi-modal models for video, the mixture of dynamic textures and the layered dynamic texture, which provide principled frameworks for video clustering and motion segmentation. We also propose a non-linear model, the kernel dynamic texture, which can capture complex patterns of motion through a non-linear manifold embedding. Finally, we demonstrate the applicability of these models to a wide variety of real-world computer vision problems, including motion segmentation, video clustering, video texture classification, highway traffic monitoring, crowd counting, and adaptive background subtraction. We also demonstrate that the dynamic texture is a suitable representation for musical signals, by applying the proposed models to the computer audition task of song segmentation. These successes validate the dynamic texture framework as a principled approach for representing video, and suggest that the models could be useful in other domains, such as computer audition, that require the analysis of time-series data.

BIOGRAPHY:

Antoni Chan is a postdoctoral researcher in the Statistical Visual Computing Lab at the University of California, San Diego (UCSD). He earned his Ph.D. in Electrical and Computer Engineering department from UCSD in 2008, and his B.S. and M.Eng. in Electrical Engineering from Cornell University in 2000 and 2001. From 2001 to 2003, he was a visiting scientist in the Vision and Image Analysis Lab at Cornell University. From 2006-2008, he was the recipient of an NSF IGERT fellowship. His research interests are in computer vision and machine learning. He aims to develop statistical models for images and video, which have applications to a wide variety of computer vision problems, such as traffic surveillance, crowd monitoring, and semantic image annotation.

Enquiries: Miss Temmy So at tel 2609 8444

For more information, please refer to http://www.cse.cuhk.edu.hk/seminar