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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/1467

Title: AUTOMATIC SEMANTIC VIDEO OBJECT SEGMENTATION
Authors: Eneyachew, Tamir
Advisors: Dr Kumudha Raimond
Keywords: content-based video processing
automatic segmentation
semantic video objects
change detection
motion estimation
post-processing
Copyright: Dec-2007
Date Added: 17-Oct-2008
Publisher: Addis Ababa University
Abstract: Content-based video processing is one of the methods considered to meet the demands of newly emerging multimedia applications. For content-based processing, video has to be segmented into meaningful objects – semantic video objects. Many applications require automatic segmentation (AS) of semantic video objects. However, AS is very challenging task. In this thesis, an effective AS system is proposed by examining, selecting and combining efficient and simplified techniques that are justified with theoretical analysis. The proposed system is developed and tested with the following three cases depending on whether there exists camera motion or not in video sequences, and whether there is an initial background reference frame. Case-1 is for video sequences with no camera motion and with initial background reference frame, Case-2 is for video sequences where there is no camera motion and no initial background reference frame, and Case-3 is for video sequences with camera motion. Change detection is used as the main step in each of the cases to detect semantic objects and to produce object mask. Different problems in change detection like “uncovered background”, “global motion of background (GMOB)” and “camera noise” are identified and solved. Two change detection results are combined to remove the uncovered background problem. More emphasis is made for the problem of GMOB. A 3-level block-based hierarchical motion estimation and affine parameter model for frame warping is used to solve this problem. Camera noise is removed by using model-based change detection. For post-processing to improve the resulting change detection masks, a new filling-in technique is proposed. This technique is used to fill open areas inside object regions with uniform intensity. To improve the boundary of segmentation masks, morphological open close operations are used. The final semantic video objects are obtained by superimposing the resulting mask over the original frame. x Test results show that the system effectively identified and segmented the semantic objects. Subjective evaluation of results for the three cases showed that among the window sizes used in change detection, 5x5 and 7x7 produced better and comparable results in terms of visual quality and boundary smoothness. These results are obtained after applying one pair of open close operation in Case-1 and two pairs in Case-2 and Case-3. Based on subjective comparison of results with other systems, 80% of observations for the results of Case-1 and 100% for Case-2 reported more pleasing results with smooth boundary.
Description: A thesis submitted to the school of Graduate studies of Addis Ababa University in partial fulfillment of the requirements for the degree of Masters of Science in Computer Engineering
URI: http://hdl.handle.net/123456789/1467
Appears in:Thesis - Computer Engineering

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