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Noise Resilient Thresholding Based on Fuzzy Logic and Non-linear Filtering

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Machine Learning for Intelligent Multimedia Analytics

Part of the book series: Studies in Big Data ((SBD,volume 82))

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Abstract

This chapter proposes a novel image thresholding technique resilient to noise based on the fuzzy logic and the non-linear filtering. The rationale of the proposed technique is to use adaptive Kuwahara filtering, which essentially suppresses the noise while retaining the image texture information. Finally, an optimized threshold selection process is formalized using the fuzzy logic properties. The performance of the proposed technique is then evaluated by extensive experiments on different types of synthetic, real-world images, and floor plan images. Finally, the superiority of the proposed technique is carried out by detailed comparison with the existing methods.

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Authors and Affiliations

  1. Indian Institute of Technology Jodhpur, Jodhpur, India

    Shreya Goyal, Gaurav Bhatnagar & Chiranjoy Chattopadhyay

Authors
  1. Shreya Goyal
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  2. Gaurav Bhatnagar
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  3. Chiranjoy Chattopadhyay
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Corresponding author

Correspondence to Shreya Goyal .

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Editors and Affiliations

  1. Department of Computer Science and Engineering and Information Technology, Jaypee University of Information Technology, Solan, Himachal Pradesh, India

    Pardeep Kumar

  2. Department of Computer Science and Engineering, National Institute of Technology, Patna, Bihar, India

    Amit Kumar Singh

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Goyal, S., Bhatnagar, G., Chattopadhyay, C. (2021). Noise Resilient Thresholding Based on Fuzzy Logic and Non-linear Filtering. In: Kumar, P., Singh, A.K. (eds) Machine Learning for Intelligent Multimedia Analytics. Studies in Big Data, vol 82. Springer, Singapore. https://doi.org/10.1007/978-981-15-9492-2_7

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