Abstract
Image segmentation is known as one of the most critical task in image processing and pattern recognition in contemporary time, for this purpose Multi Level Thresholding based approach has been an acclaimed way out. Endeavor of this paper is to focus on obtaining the optimal threshold points by using Tsallis Entropy. In this paper, we have incorporated a Differential Evolution (DE) based technique to acquire optimal threshold values. Furthermore, results are compared with two state-of-art algorithms- a. Particle Swarm Optimization (PSO), and b. Genetic Algorithm (GA). Several image quality assessment indices are applied for the performance analysis of the outcome derived by applying the proposed algorithm. © 2012 Springer-Verlag.
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Sarkar, S., Das, S., & Chaudhuri, S. S. (2012). Multilevel image thresholding based on Tsallis entropy and differential evolution. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7677 LNCS, pp. 17–24). https://doi.org/10.1007/978-3-642-35380-2_3
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