{"title":"Special session on Statistical Image analysis for computer-aided detection and diagnosis on medical and biological images (SIA-MBI)","authors":"M. Adel, S. Bourennane","doi":"10.1109/IPTA.2014.7001920","DOIUrl":"https://doi.org/10.1109/IPTA.2014.7001920","url":null,"abstract":"","PeriodicalId":406232,"journal":{"name":"International Conference on Image Processing Theory Tools and Applications","volume":"37 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120967842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Michel Abboud, A. Benzinou, K. Nasreddine, M. Jazar
{"title":"Shape restoration for robust tangent principal component analysis","authors":"Michel Abboud, A. Benzinou, K. Nasreddine, M. Jazar","doi":"10.1109/IPTA.2015.7367190","DOIUrl":"https://doi.org/10.1109/IPTA.2015.7367190","url":null,"abstract":"Shape outliers can seriously affect the statistical analysis of the shape variations usually performed by the Principal Component Analysis PCA. This paper presents an algorithm for outliers detection and shape restoration as a new strategy for robust statistical shape analysis. The proposed framework is founded on an elastic metric in the shape space to cope with the nonlinear shape variability. The main contribution of this work is then a formulation of a robust PCA which describes main variations associated to correct shapes without outlier effects. The efficiency of this approach is demonstrated by an evaluation carried out on HAND-Kimia and HEART-Kimia databases.","PeriodicalId":406232,"journal":{"name":"International Conference on Image Processing Theory Tools and Applications","volume":"170 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114098346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A New Generative Adversarial Network for Texture Preserving Image Denoising","authors":"Zhiping Qu, Yuanqi Zhang, Yi Sun, Xiangbo Lin","doi":"10.1109/IPTA.2018.8608126","DOIUrl":"https://doi.org/10.1109/IPTA.2018.8608126","url":null,"abstract":"In this paper, a new generative adversarial networks (GAN) is proposed for image denoising. The proposed GAN has a new generator network to produce denoised images with noisy images as input, and the entire network is trained using a new loss to represent the distance between the data distribution of clean images and denoised images. Based on quantitative and qualitative evaluating criteria, we made comparisons between our method and other denoising methods which shows the superiority of our approach. Keywords—Image denoising, Generative adversarial network, Loss function, Texture Preserving.","PeriodicalId":406232,"journal":{"name":"International Conference on Image Processing Theory Tools and Applications","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114348501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}