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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Khan, S. Shah, M. |
| Copyright Year | 2001 |
| Description | Author affiliation: Comput. Vision Lab., Univ. of Central Florida, Orlando, FL, USA (Khan, S.; Shah, M.) |
| Abstract | Video segmentation is different from segmentation of a single image. While several correct solutions may exist for segmenting a single image, there needs to be a consistency among segmentations of each frame for video segmentation. Previous approaches of video segmentation concentrate on motion, or combine motion and color information in a batch fashion. We propose a maximum a posteriori probability (MAP) framework that uses multiple cues, like spatial location, color and motion, for segmentation. We assign weights to color and motion terms, which are adjusted at every pixel, based on a confidence measure of each feature. We also discuss the appropriate modeling of PDFs of each feature of a region. The correct modeling of the spatial PDF imposes temporal consistency among segments in consecutive frames. This approach unifies the strengths of both color segmentation and motion segmentation in one framework, and shows good results on videos that are not suited for either of these approaches. |
| Sponsorship | IEEE Comput. Soc. Tech. Committee on Pattern Analysis & Machine Intelligence |
| File Size | 941107 |
| File Format | |
| ISBN | 0769512720 |
| ISSN | 10636919 |
| DOI | 10.1109/CVPR.2001.991039 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2001-12-08 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image segmentation Computer vision Video compression Image coding Layout Motion segmentation Visualization Laboratories Motion measurement Codecs |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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