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Erschienen in:

2017 | OriginalPaper | Buchkapitel

Tsallis Entropy Based Image Thresholding for Image Segmentation

verfasst von : M. S. R. Naidu, P. Rajesh Kumar

Erschienen in: Computational Intelligence in Data Mining

Verlag: Springer Singapore

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Abstract

Image segmentation is a method of segregating the image into required segments/regions. Image thresholding being a simple and effective technique, mostly used for image segmentation and these thresholds are optimized by optimization techniques by maximizing the Tsallis entropy. However, as the two level thresholding is extended to multi-level thresholding, the computational complexity of the algorithm is further increased. So there is need of evolutionary and swarm optimization techniques. In this paper, first time optimal thresholds are obtained by maximizing the Tsallis entropy using novel adaptive cuckoo search algorithm (ACS). The proposed ACS algorithm performance of image segmentation is tested using natural and standard images. Experiments shows that proposed ACS is better than particle swarm optimization (PSO) and cuckoo search (CS).

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Titel: Tsallis Entropy Based Image Thresholding for Image Segmentation
verfasst von: M. S. R. Naidu
P. Rajesh Kumar
Verlag: Springer Singapore
Buch: Computational Intelligence in Data Mining
Print ISBN: 978-981-10-3873-0

Electronic ISBN: 978-981-10-3874-7

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-981-10-3874-7_34

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