Distributed Image and Video Processing:
Directed Graphs, Bayesian Estimation, and Hidden Markov Models
报告时间:2007年5月16日上午10点
报告地点:信电楼4楼学术报告厅
报告人:Dan Schonfeld教授,美国伊利诺大学芝加哥校区电子与计算机工程系多媒体通信实验室的Co-Director
Traditional image and video processing algorithm are centralized. They rely on an implementation on a large server for efficient processing. However, with the emergence of large camera networks, we must design distributed algorithms that can scale with the size of the network and number of targets. In this talk, we present a general methodology to the design of distributed image and video processing system. The premise of our approach to distributed processing is the graphical decomposition of complex dynamical systems. We provide a distributed Bayesian approach to multi-object tracking and multi-camera tracking. Implementation of the proposed approach to distributed Bayesian tracking is achieved using a particle filtering framework. In this framework, multiple particle filters associated with individual targets and cameras collaborate to obtain the joint Bayesian estimate. We subsequently present a distributed multi-dimensional hidden Markov model (HMM). A complex non-casual multi-dimensional HMM is characterized by multiple distributed causal HMMs. The training and classification algorithms of the causal HMMs are derived by extension of the expectation-maximization (EM), general forward-backward (GFB), and Viberbi algorithms to multi-dimensional systems. We use the proposed HMM for multiple interactive motion trajectory classification and natural-man-made image segmentation.
Dan Schonfeld博士的简历
Schonfeld博士