The entropy image thresholding technique is much in demand today for image segmentation. Furthermore, population algorithm aided thresholding techniques have been proven previously to be extremely effective in producing better results. In this work, we have concentrated on the minimum cross-entropy criterion for image segmentation. The objective of this work is to demonstrate the capability of Cuckoo Search Optimization-based Minimum Cross-Entropy Technique. The algorithm has been compared against old algorithms GA and PSO. Results have been assimilated in this work. The results have clearly demonstrated the competence of Cuckoo Search Optimization algorithm in assisting Cross Entropy-based thresholding procedure. © Springer India 2015.
CITATION STYLE
Roy, S., Kumar, U., Chakraborty, D., Nag, S., Mallick, A., & Dutta, S. (2015). Comparative Analysis of Cuckoo Search Optimization-Based Multilevel Image Thresholding. In Advances in Intelligent Systems and Computing (Vol. 309 AISC, pp. 327–342). Springer Verlag. https://doi.org/10.1007/978-81-322-2009-1_38
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