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Detection and removal of impulse noise for cattle identification using fuzzy methods

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Author: 
Anusha Edwin and Jerrin Thomas Panachakel
Abstract: 

Muzzle patterns of cattle are uneven features of their skin surface. They are different from each other like finger prints of human. Hence these muzzle patterns can be used to identify cattle. Noise is any unwanted component in an image. It is important to eliminate noise in the images before some sub-sequent processing such as edge detection, image segmentation and pattern recognition. This paper proposes a method for salt and pepper noise removal based on mathematical methods using fuzzy rules from muzzle images. The proposed Fuzzy operator consists of two modules viz. Detection module and Adaptation module. For fuzzy reasoning, a triangular shaped fuzzy set described by a two parameter membership function is used. For each pixel element 13 fuzzy rules are applied and an output y is produced in detection module. In adaptation module y is further reduced and it is added with input pixel value to get output pixel value. The proposed method is able to perform a very strong noise cancellation while preserving muzzle image details. The Fuzzy Filter is compared with other non-linear filters such as Median Filter, SDROM Filter, PSM Filter and is getting better results in terms of PSNR values and SSIM Values.

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