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A hybrid fuzzy filtering - fuzzy thresholding technique for region of interest detection in noisy images

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Abstract

Noise leads to the ambiguity in regions of interest detection by corrupting the pixel information and is a vital problem in image processing domain. A novel hybrid technique based on fuzzy filtering and fuzzy thresholding is proposed here to extract the object regions accurately in presence of Gaussian noises. The proposed method is automated, does not need any parameter tuning as well does not need prior knowledge of the image or noise. An asymmetrical triangular fuzzy filter with median center coupled with a thresholding based on fuzziness minimization technique are implemented for this purpose. The fuzzy thresholding technique helps to classify the pixels with low signal-to-noise ratio (SNR) caused either due to noise or by the application of noise removal process. The proposed technique is applied in benchmark images corrupted by noises and are compared with some of the popular algorithms of object detection. The results indicate that the proposed method has superior performance in terms of peak signal-to-noise ratio (PSNR) and mean square error (MSE) value for images corrupted with Gaussian noises with standard deviation upto 1.5.

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Notes

  1. https://mmcheng.net/msra10k/

  2. http://saliencydetection.net/dut-omron/

  3. http://www.vision.caltech.edu/Image_Datasets/Caltech256/

  4. http://ivipc.uestc.edu.cn/project/FanmanMengSMCB/

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Acknowledgements

The financial support received under DST INSPIRE Faculty grant is thankfully acknowledged.

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Authors and Affiliations

  1. Discipline of Astronomy, Astrophysics and Space Engineering, Indian Institute of Technology, Indore, Simrol, Indore, 453552, India

    Sanmoy Bandyopadhyay, Saurabh Das & Abhirup Datta

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  1. Sanmoy Bandyopadhyay
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  2. Saurabh Das
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  3. Abhirup Datta
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Correspondence to Saurabh Das.

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Bandyopadhyay, S., Das, S. & Datta, A. A hybrid fuzzy filtering - fuzzy thresholding technique for region of interest detection in noisy images. Appl Intell 50, 1112–1132 (2020). https://doi.org/10.1007/s10489-019-01551-z

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