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Multilevel Image Thresholding Based on Tsallis Entropy and Differential Evolution

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7677)

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.

Keywords

  • Multilevel Image Segmentation
  • Tsallis Entropy
  • Differential Evolution
  • MSSIM
  • WPSNR

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References

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© 2012 Springer-Verlag Berlin Heidelberg

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Sarkar, S., Das, S., Chaudhuri, S.S. (2012). Multilevel Image Thresholding Based on Tsallis Entropy and Differential Evolution. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2012. Lecture Notes in Computer Science, vol 7677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35380-2_3

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  • DOI: https://doi.org/10.1007/978-3-642-35380-2_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35379-6

  • Online ISBN: 978-3-642-35380-2

  • eBook Packages: Computer ScienceComputer Science (R0)