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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Zhou Xiao Zhang Guohua Zhang Guilin |
| Copyright Year | 2008 |
| Description | Author affiliation: Inst. for Pattern Recognition & Artificial Intell., Huazhong Univ. of Sci. & Technol., Wuhan (Zhou Xiao; Zhang Guilin) || AVICI, Luoyang Inst. of Electro-Opt. Equip., Luoyang (Zhang Guohua) |
| Abstract | An approved CFAR (constant false alarm rate) method based on half side Gaussian model (HG-CFAR) is presented for the target segmentation in infrared image. Firstly, the distribution of the residual image after preprocessing based on morphological filters is exploited. Although widely used and useful, the traditional Gaussian distribution does not appear to be the best choice for modeling the residual images. Half side Gaussian distribution (HG) is adopted to model the residuals since it fits the data better. Then, based on the HG model a new CFAR threshold method called HG-CFAR is proposed. Secondly, a novel algorithm for model parameter estimation is given. The estimation algorithm has at least two merits: one is very simple and efficient can be realized on hardware easily. Another is robust to the number and size of the targets. At last, comparisons are made between HG-CFAR and traditional CFAR based on Gaussian distribution. The results show that the new method is more efficient. |
| Starting Page | 877 |
| Ending Page | 881 |
| File Size | 413458 |
| Page Count | 5 |
| File Format | |
| ISBN | 9780769534909 |
| DOI | 10.1109/PACIIA.2008.329 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-12-19 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Infrared detectors HG-CFAR Parameter estimation infrared small target Gaussian distribution Mercury (metals) Image segmentation Filters Object detection Infrared imaging Hardware Robustness small target detection |
| Content Type | Text |
| Resource Type | Article |
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