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Multilevel Image Thresholding Established on Fuzzy Entropy Using Differential Evolution

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Hybrid Intelligent Systems (HIS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 734))

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Abstract

This study establishes a new methodology based on fuzzy partition of the image histogram and entropy for multi-level image thresholding. We propose a new methodology which is implemented in framework of the multi-step segmentation of image format. Further framework is improved to attain improved threshold value. A meta-heuristic Differential Evolution (DE) algorithm is cast-off in a direction to resolve the optimization problem, that results in a more rapid and accurate conjunction headed for the ideal situation. The accomplishment of DE can also be measured in reference to more or less widely held universal optimization procedures like Genetic Algorithms and Particle Swarm Optimization. Simulation results are equated with other entropy technique like Shannon entropy for the purpose of establishing the distinguishable difference in image.

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Authors and Affiliations

  1. Amity University, Noida, India

    Abhishek Dixit & Sushil Kumar

  2. Indian Institute of Technology, Roorkee, India

    Millie Pant

  3. Rajiv Gandhi Institute of Petroleum Technology, Noida, India

    Rohit Bansal

Authors
  1. Abhishek Dixit
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  2. Sushil Kumar
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  3. Millie Pant
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  4. Rohit Bansal
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Corresponding author

Correspondence to Abhishek Dixit .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. Department of Computer Science, South Asian University, Chanakyapuri, Delhi, India

    Pranab Kr. Muhuri

  3. Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia

    Azah Kamilah Muda

  4. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

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Dixit, A., Kumar, S., Pant, M., Bansal, R. (2018). Multilevel Image Thresholding Established on Fuzzy Entropy Using Differential Evolution. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Hybrid Intelligent Systems. HIS 2017. Advances in Intelligent Systems and Computing, vol 734. Springer, Cham. https://doi.org/10.1007/978-3-319-76351-4_9

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  • DOI: https://doi.org/10.1007/978-3-319-76351-4_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76350-7

  • Online ISBN: 978-3-319-76351-4

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