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A fast recurring two-dimensional entropic thresholding algorithm
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DOI：10.1016/S0031-3203(97)00158-1.png)
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Abstract
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Thresholding is an important form of image segmentation and is used in the processing of images for many applications. One of the criteria to select a suitable threshold is the maximization of the two-dimensional (2-D) entropies based on the 2-D (gray-level/local average gray-level) histogram. The rationale of this approach is introduced. In order to reduce the computation time of entropy function, a fast recurring algorithm for 2-D entropic thresholding method is presented. The experimental results show that the processing time to obtain the threshold vector from 2-D histogram is reduced from 30 to 0.15 s. (C) 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Keywords:
threshold
two-dimensional entropies
segmentation
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