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Rough-Fuzzy Collaborative Multi-level Image Thresholding: A Differential Evolution Approach

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 378))

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Abstract

In this article, a granular computing based multi-level gray image thresholding algorithm is presented. An image is divided into spatial blocks called granules, and the classes of gray levels are represented using a fuzzy-rough collaborative approach, where the measure of roughness of a rough set is also modified from the classical definition of rough sets. This measure for each rough set is minimized simultaneously to obtain the optimal thresholds. Tchebycheff decomposition approach is employed to transform this multi-objective optimization problem to a single objective optimization problem. Differential Evolution (DE), one of the most efficient evolutionary optimizers of current interest, is used to optimize this single objective function, thus reducing the execution time. Superiority of the proposed method is presented by comparing it with some popular image thresholding techniques. MSSIM index and Probabilistic Rand Index (PRI) are used for quantitative comparison on the Berkley Image Segmentation Data Set (BSDS300).

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Authors and Affiliations

  1. Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, 700032, India

    Sujoy Paul

  2. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, 700108, India

    Shounak Datta & Swagatam Das

Authors
  1. Sujoy Paul
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  2. Shounak Datta
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  3. Swagatam Das
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Corresponding author

Correspondence to Swagatam Das .

Editor information

Editors and Affiliations

  1. Faculty of Mechanical Engineering, Department of Applied Computer Science, Brno University of Technology, Brno, Czech Republic

    Radek Matoušek

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Paul, S., Datta, S., Das, S. (2015). Rough-Fuzzy Collaborative Multi-level Image Thresholding: A Differential Evolution Approach. In: Matoušek, R. (eds) Mendel 2015. ICSC-MENDEL 2016. Advances in Intelligent Systems and Computing, vol 378. Springer, Cham. https://doi.org/10.1007/978-3-319-19824-8_27

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  • DOI: https://doi.org/10.1007/978-3-319-19824-8_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19823-1

  • Online ISBN: 978-3-319-19824-8

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