| Summary: | 博士 === 國立交通大學 === 資訊科學與工程研究所 === 99 === Bi-layer video segmentation, i.e., the extraction of foreground regions from background ones for a video sequence, is a challenging research field in computer vision due to large content variation among video frames. To better address this bi-layer video segmentation problem, three research topics are investigated in this thesis including background model initialization, background model maintenance, and video layer propagation. While the first two topics concern static background modeling for analyzing videos obtained from static cameras, the third one pertains to dynamic foreground segmentation for videos captured by moving cameras.
For the problem of background model initialization, we propose an efficient background model estimation scheme based on image block classification, and develop novel criteria for measuring the completeness of a background model. For the problem of background model maintenance, we look into the formulations of Gaussian mixture modeling (GMM) and identify the needs of two types of learning rates for GMM to effectively deal with a trade-off between robustness to background changes and sensitivity to foreground abnormalities. A novel bivariate learning rate control scheme for GMM based on a feedback of high-level information is also proposed. For the problem of video layer propagation, a new framework based on semi-supervised spectral clustering is proposed for dynamic foreground segmentation of a video shot captured by a moving camera. The adopted formulation of semi-supervised spectral clustering is generalized to regularize the reliabilities of layer labels in sequential propagation. Experimental results show that satisfactory results of related bi-layer video analysis can indeed be obtained with the proposed approaches.
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