Gray level thresholding using the Havrda and Charvat entropy

N. Pavesic, S. Ribaric
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引用次数: 27

Abstract

Investigating the Kapur et al. (1985) image thresholding method, we found, that taking the sum of the Havrda and Charvat entropies as a criterion for threshold selection instead of the Shannon entropies, can result in a better image segmentation in the sense of greater uniformity of the partitioned segments, as well as greater contrast among segments.
灰度阈值使用Havrda和Charvat熵
通过对Kapur等人(1985)图像阈值分割方法的研究,我们发现,采用Havrda和Charvat熵的总和作为阈值选择的标准,而不是Shannon熵作为阈值选择的标准,可以获得更好的图像分割效果,即分割后的片段更加均匀,片段之间的对比度也更大。
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