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| Maximum Entropy Image Thresholding Based on Two-Dimensional Histogram Oblique Segmentation |
| WU Yi-Quan, PAN Zhe, WU Wen-Yi |
| School of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016 |
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Abstract The obvious wrong segmentation is pointed out in the existing two-dimensional histogram vertical segmentation method. A two-dimensional histogram oblique segmentation method is proposed. Then the formula and its fast recursive algorithm of the maximum Shannon entropy thresholding are deduced based on the two-dimensional histogram oblique segmentation. Finally, the threshold images and the processing time are given in the experimental results and analysis. The results are compared with those of the original maximum Shannon entropy algorithm and its fast algorithms based on the two-dimensional histogram vertical segmentation. The experimental results show that the proposed method makes the inner part uniform and the edge accurate in the threshold image, and it has a better anti-noise property. The processing time of the fast recursive algorithm of the proposed method is about 2% of that of the original two-dimensional maximum Shannon entropy algorithm, and it is less than one third of that of two fast recursive algorithms of the maximum Shannon entropy thresholding based on the two-dimensional histogram vertical segmentation.
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Received: 14 September 2007
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2009, Vol. 22 
