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New Divergence and Entropy Measures for Intuitionistic Fuzzy Sets on Edge Detection

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Abstract

Edges of the image play an important role in the field of digital image processing and computer vision. The edges reduce the amount of data, extract useful information from the image and preserve significant structural properties of an input image. Further, these edges can be used for object and facial expression detection. In this paper, we will propose new intuitionistic fuzzy divergence and entropy measures with its proof of validity for intuitionistic fuzzy sets. A new and significant technique has been developed for edge detection. To check the robustness of the proposed method, obtained results are compared with Canny, Sobel and Chaira methods. Finally, mean square error (MSE) and peak signal-to-noise ratio (PSNR) have been calculated and PSNR values of proposed method are always equal or greater than the PSNR values of existing methods. The detected edges of the various sample images are found to be true, smooth and sharpen.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, HP, India

    Mohd Dilshad Ansari

  2. Department of Mathematics, ITM University, Gwalior, MP, 474001, India

    Arunodaya Raj Mishra

  3. Department of Electronics and Telecommunication, G. H. Raisoni College of Engineering, Nagpur, India

    Farhina Tabassum Ansari

Authors
  1. Mohd Dilshad Ansari
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  2. Arunodaya Raj Mishra
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  3. Farhina Tabassum Ansari
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Correspondence to Arunodaya Raj Mishra.

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Ansari, M.D., Mishra, A.R. & Ansari, F.T. New Divergence and Entropy Measures for Intuitionistic Fuzzy Sets on Edge Detection. Int. J. Fuzzy Syst. 20, 474–487 (2018). https://doi.org/10.1007/s40815-017-0348-4

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