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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Li, Bo Tang, Zhen Yuan, Baozong Miao, Zhenjiang |
| Copyright Year | 2008 |
| Abstract | Robust detection of moving objects in complex scenes is one of the most challenging issues in computer vision. In this paper, we present a novel texture-wise approach to segment moving objects with codebook and local binary patterns (LBP). In many moving segmentation algorithms, the information from limited frames before current image is used. Our approach models background over long time with small memory. Firstly, we construct codebook model which represents a compressed form of background model for long image sequences. A single Gaussian model of per-pixel is built to deal with illumination changes. By using the correlation and texture of spatially proximal pixels, local binary patterns background model is constructed. Finally current image is segmented into two parts, foreground and background, by comparing current image with background model. Experiments show that the proposed approach achieves promising results robustly in real videos. |
| Starting Page | 239 |
| Ending Page | 243 |
| File Size | 447282 |
| Page Count | 5 |
| File Format | |
| ISBN | 9780769531199 |
| DOI | 10.1109/CISP.2008.653 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-05-27 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image segmentation Computer vision Image coding Layout Lighting Object detection Noise robustness Image sequences History Background noise |
| Content Type | Text |
| Resource Type | Article |
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