Computer Vision and Image Understanding
Computer Vision and Image Understanding
EURASIP Journal on Applied Signal Processing
Pattern Recognition Letters
Applying the multi-category learning to multiple video object extraction
Pattern Recognition
Evaluation of video news classification techniques for automatic content personalisation
International Journal of Advanced Media and Communication
Computer Vision and Image Understanding
Computer Vision and Image Understanding
Video news classification for automatic content personalization: a genetic algorithm based approach
Proceedings of the 14th Brazilian Symposium on Multimedia and the Web
Semi-automatic video object segmentation using seeded region merging and bidirectional projection
Pattern Recognition Letters
Video segmentation for markerless motion capture in unconstrained environments
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Moving object segmentation: a block-based moving region detection approach
PCM'04 Proceedings of the 5th Pacific Rim conference on Advances in Multimedia Information Processing - Volume Part I
3D information extraction using Region-based Deformable Net for monocular robot navigation
Journal of Visual Communication and Image Representation
A new tracking mechanism for semi-automatic video object segmentation
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
An adaptive sample count particle filter
Computer Vision and Image Understanding
Video object tracking via central macro-blocks and directional vectors
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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Video object segmentation and tracking are essential for content-based video processing. This paper presents a framework for a semiautomatic approach to this problem. A semantic video object is initialized with human assistance in a key frame. The video object is then tracked and segmented automatically in the following frames. A new active contour model, VSnakes, is introduced as a segmentation method in this framework. The active contour energy is defined so as to reflect the energy difference between two contours instead of the energy of a single contour. Multiple-resolution wavelet decomposition is applied in generating the edge energy of the image frame. Contour relaxation is used to deal with the object deformation frame by frame, and the Viterbi algorithm is used to update the contour path during contour relaxation. Compared to the original snakes algorithm, semiautomatic video object segmentation with the VSnakes algorithm resulted in improved performance in terms of video object shape distortion (1.4% versus 2.9% in one experiment), which suggests that it could be a useful tool in many content-based video applications, e.g., MPEG-4 video object generation and medical imaging.