Abstract
Entropic thresholding algorithms are important tools in image processing. In this article, fast iterative methods are derived for the minimum cross entropy thresholding and maximum entropy thresholding algorithms using the one-point iteration scheme. Simulations performed using systhetic generated histograms and a real image show the speed advantage and the accuracy of the iterated methods.
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© 2003 Springer-Verlag Berlin Heidelberg
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Li, C.H., Lee, C.K., Tam, P.K.S. (2003). Entropic Thresholding Algorithms and their Optimizations. In: Karmeshu (eds) Entropy Measures, Maximum Entropy Principle and Emerging Applications. Studies in Fuzziness and Soft Computing, vol 119. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36212-8_10
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DOI: https://doi.org/10.1007/978-3-540-36212-8_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05531-7
Online ISBN: 978-3-540-36212-8
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