An iterative algorithm for minimum cross entropy thresholding
Pattern Recognition Letters
Image thresholding using Tsallis entropy
Pattern Recognition Letters
Engineering Applications of Artificial Intelligence
Information Sciences: an International Journal
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Novel KNN-motivation-PSO and its application to image segmentation
Proceedings of the CUBE International Information Technology Conference
Multilevel image thresholding based on tsallis entropy and differential evolution
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
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Image entropy thresholding is one of the most widely used technique for multilevel thresholding. The endeavor of this paper is to focus on obtaining the optimal threshold points. Several meta-heuristics are being applied in literatures over the decade, for improving the accuracy and computational efficiency of Minimum Cross Entropy Thresholding (MCET) method. In this paper, we have incorporated a Differential Evolution (DE) based approach towards image segmentation. Results are also compared with modern state-of-art algorithms like Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Further Mean Structural Similarity Index Measurement (SSIM) and Universal Image Quality Index (UIQI) are also being used for performance evaluation.