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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Al-Osaimi, G. El-Zaart, A. |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of Comput. Sci., King Saud Univ., Riyadh (Al-Osaimi, G.; El-Zaart, A.) |
| Abstract | Vision plays the most important role in human perception, which is limited to only the visual band of the electromagnetic spectrum. Therefore, the need for Radar imaging systems, to recover some sources that are not within human visual band, is raised. This paper presents a new algorithm for Synthetic Aperture Radar (SAR) images segmentation based on thresholding technique. Generally, segmentation of a SAR image falls into two categories; one based on grey levels and the other based on texture. The present paper deals with SAR images segmentation based on grey levels. We developed a new formula using Minimum Cross Entropy Thresholding (MCET) method for estimating optimal threshold value based on Gamma distribution to analyzing data on images; that means histogram of SAR images is assumed to be a mixture of Gamma distributions. The proposed method is iterative which decreases the number of operation to converge tends to the optimal solution. It is applied on bi-modal and multimodal scenarios. The results obtained are promising. |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 829175 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424417513 |
| DOI | 10.1109/ICTTA.2008.4530133 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-04-07 |
| Publisher Place | Syria |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image Thresholding Minimum Cross Entropy Data analysis SAR Images Gamma Distribution Humans Entropy Image segmentation Histograms Multimodal thresholding Image analysis Radar imaging Iterative algorithm Electromagnetic spectrum Iterative methods Synthetic aperture radar |
| Content Type | Text |
| Resource Type | Article |
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