Research On Algorithm Of Video Object Segmentation

Video object segmentation is one of the most important techniques for MPEG-4 content-based coding scheme, which has many important applications in content-base index, object recognition, object tracking, video phone, video conference and interactive multimedia. Many problems of computer vision are solved by using the techniques of video object segmentation. So the study on video object segmentation techniques has important significance and application value, which is one of the most difficult problems in the multimedia techniques.In this thesis, the novel methods are explored and investigated for video object segmentation. The main results are as follows:1.The related theories and methods of video object segmentation have been analyzed. Firstly, the theory of mode clustering and the fuzzy c-means clustering method are studied, the Bayes classifier, the maximum a posteriori probability model and Markov random field are discussed, secondly, the morphological dilation, erosion and open-close operations are introduced, the nerve cell model, neural network classifier and BP training algorithm are analyzed, finally, the six-parameter affine motion model of global motion estimation, the Gauss-Newton iteration method and the Levenberg-Marquardet optimization method are studied.2.The edges of objects are used to correct the contours in video object segmentation, in order to obtain the precise contours of video objects, so edge extracton is very important in video object segmentation. We analyze clone technique, and suggest a clone algorithm, then apply the algorithm to edge extraction of color images, finally, we propose an effective edge extraction algorithm by integrating edge connection method. Automatic threshold choice is difficult and important to color image edge detection. After human visual properties have been analyzed, an algorithm of automatic threshold choice in image edge detection is proposed. Experiments demonstrate the effectiveness of our algorithm in comparison with other related methods3.The video object segmentation based on temporal or spatial information is researched, then an effective video object segmentation algorithm is suggested. Firstly, a group of pictures are inputted, the initial frame difference image is obtained by employing the frame difference information, and the initial frame difference mask is obtained by integrating fuzzy c-means clustering and genetic algorithm. The initial motion mask is obtained by using motion estimation. Secondly, the coarse initial object mask is obtained by employing the initial frame difference and motion mask. The mask is filled and corrected by using the edges of objects in the motion window. An initial video object is obtained by using the mask and the original image data. Finally the object tracking is executed by using motion estimation and background information. Experiments demonstrate the effectiveness of our algorithm in comparison with other related methods4.After the frame difference method and the background difference one have been researched, an algorithm of automatic video object segmentation based on background constructing is suggested. A group of key background images are obtained by employing many extracted key frames based on motion information, and the background of video sequence is obtained by employing these key background images. After the background difference images have been obtained, the object masks are obtained by using BP neural network classifier. The masks are improved by using the post-processing procedures, such as mask filling, noise elimination and contour smoothing. The video objects are extracted by using the processed masks. For the video sequence with motion background, the global motion estimation and compensation is used, the background is constructed by the median of a group of compensated video frames. Finally, the object segmentation and tracking is executed by employing the background difference method. Experiments demonstrate the effectiveness of our algorithm in comparison with other related methods.5.A lot of semiautomatic video object segmentation methods are analyzed, an algorithm of semiautomatic video object segmentation by integrating background constructing and motion estimation is suggested. Firstly, a coarse contour of the object is obtained by employing manual method; the contour is corrected by employing edges of the object. Secondly, the corrected contour is filled and processed by employing smoothing method, and the initial object and the background are obtained by using the processed contour. The object masks of subsequence frames are obtained by employing background difference algorithm, and the masks are corrected by using motion estimation. Finally, the object tracking is executed by employing background updating and background difference method. Experiments demonstrate the effectiveness of our algorithm in comparison with other related methods.