Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/TCSVT.2002.808089 | IEEE Trans. Circuits Syst. Video Techn. |
Keywords | Field | DocType |
video signal processing,semantic video object,content-based video applications,active contour model,mpeg-4 video object generation,wavelet transforms,vsnakes algorithm,video object tracking,energy difference,image resolution,image segmentation,content-based video processing,multiple-resolution wavelet decomposition,video object segmentation,image frame,video object,active contour energy,content-based video application,semiautomatic video object segmentation,object deformation frame,viterbi algorithm,medical imaging,video object shape distortion,contour relaxation,indexing terms,sun,videoconference,video compression,video processing,transform coding,active contour | Active contour model,Computer vision,Block-matching algorithm,Video processing,Pattern recognition,Computer science,Segmentation,Image processing,Image segmentation,Video tracking,Artificial intelligence,Key frame | Journal |
Volume | Issue | ISSN |
13 | 1 | 1051-8215 |
Citations | PageRank | References |
31 | 1.43 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shijun Sun | 1 | 88 | 7.91 |
| D R Haynor | 2 | 171 | 22.70 |
| Yongmin Kim | 3 | 912 | 122.36 |