{"title":"Image segmentation using snake model with nosie adaptive fuzzy switching median filter and MSRM method","authors":"Sajal Pahariya, S. Tiwari","doi":"10.1109/ICCIC.2015.7435798","DOIUrl":null,"url":null,"abstract":"In this paper, we are using maximum similarity region merging(MSRM), anisotropic diffusion (AD), noise adaptive fuzzy switching median filter, active countour /snake model. In the proposed approach, work on both gray or color images. With the use of MSRM, merge the maximum similarity area/region. AD is used to smooth the image. NAFSM is used for removing noise from an image. In the last step, we used a Snake model for removing blur effect from an image. The results on peak signal noise ratio (PSNR), Mean square error (MSE), accuracy and time method give better performance in terms of brightness and contrast of the enhanced image remove noise and increase brightness.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCIC.2015.7435798","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In this paper, we are using maximum similarity region merging(MSRM), anisotropic diffusion (AD), noise adaptive fuzzy switching median filter, active countour /snake model. In the proposed approach, work on both gray or color images. With the use of MSRM, merge the maximum similarity area/region. AD is used to smooth the image. NAFSM is used for removing noise from an image. In the last step, we used a Snake model for removing blur effect from an image. The results on peak signal noise ratio (PSNR), Mean square error (MSE), accuracy and time method give better performance in terms of brightness and contrast of the enhanced image remove noise and increase brightness.