{"title":"Performance Analysis & Implementation of Adaptive GMM for Image Restoration & Segmentation","authors":"Shilpa Hatwar, A. Wanare","doi":"10.1109/ICCUBEA.2015.213","DOIUrl":null,"url":null,"abstract":"Now days we can easily captured the images and video with the advanced technologies in camera. But this images and video are get easily contaminated by noise due to the characteristics of image sensors due to this they are mostly blurred so we can loss important data. To avoid this problem we proposed an algorithm for segmentation based on Gaussian mixture model (GMM) and restoration technique with spatial smoothness constraints and transform domain techniques. The researchers worked on single type of image but the different environmental images have any noises so that it is not suitable for all images. The proposed algorithm is works on the all diverse field of images which can remove noises from images so it is compatible to all environmental conditions with calculating different image parameter. From all of this we can get the optimum solution of suitable filter for combination of image and noise for reduction of noise by comparing of all of that. Here we also present the algorithm for video segmentation & restoration.","PeriodicalId":325841,"journal":{"name":"2015 International Conference on Computing Communication Control and Automation","volume":"398 2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Computing Communication Control and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCUBEA.2015.213","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Now days we can easily captured the images and video with the advanced technologies in camera. But this images and video are get easily contaminated by noise due to the characteristics of image sensors due to this they are mostly blurred so we can loss important data. To avoid this problem we proposed an algorithm for segmentation based on Gaussian mixture model (GMM) and restoration technique with spatial smoothness constraints and transform domain techniques. The researchers worked on single type of image but the different environmental images have any noises so that it is not suitable for all images. The proposed algorithm is works on the all diverse field of images which can remove noises from images so it is compatible to all environmental conditions with calculating different image parameter. From all of this we can get the optimum solution of suitable filter for combination of image and noise for reduction of noise by comparing of all of that. Here we also present the algorithm for video segmentation & restoration.