Titlebook: Semantic Video Object Segmentation for Content-Based Multimedia Applications; Ju Guo,C.-C. Jay Kuo Book 2002 Springer Science+Business Med

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Review of Previous Work, [13, 24]. Change detection is a simple and powerful approach, which enables automatic detection of the object shape with larger non-rigid motion if the moving foreground has a different visual appearance from the background.

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Introduction,her interesting applications, such as editing and manipulation of image sequences, video surveillance, image and video indexing and retrieval applications, require an efficient algorithm for content identification and segmentation. While specific solutions exist for some applications, the desired fu

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Review of Previous Work,resholding the inter-frame difference based image features such as color and luminance. A binary mask of moving pixels is obtained by thresholding the absolute difference of images computed between consecutive frames. The binary mask is postprocessed to generate moving objects with connected regions

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Automatic Segmentation,. The block diagram of the proposed algorithm is given in Fig. 3.1. An overview of each component in the block diagram is given below. More detailed descriptions will be presented in the following subsections.

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Object Shape Postprocessing,s such as small gulfs and isthmi due to temporal and spatial signal fluctuations. These irregular features lead to a visually annoying appearance and also increase the cost of shape coding. We adopt two boundary postprocessing techniques to handle this problem. First, we use the morphological open a

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Interactive Segmentation Algorithms,xel-wise accuracy. SIVOG identifies and extracts the semantic video object from image sequence with user interaction. SIVOG is a smart system that can analyze and interpret user’s high level input to define the semantic homogeneity, and adaptively process the video content. SIVOG consists of three b

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capillaries