{"title":"Factor graphs for pixelwise illuminant estimation","authors":"Lawrence Mutimbu, A. Robles-Kelly","doi":"10.1109/IJCNN.2015.7280811","DOIUrl":null,"url":null,"abstract":"This paper presents a method to recover the pixel-wise illuminant colour for scenes lit by multiple lights. Here, we start from the image formation process and pose the illuminant recovery task in hand into an evidence combining setting. To do this, we construct a factor graph making use of the scale space of the input image and a set of illuminant prototypes. The computation of these prototypes is data driven and, hence, our method is devoid of libraries or user input. The use of a factor graph allows for the illuminant estimates at different scales to be recovered making use of a maximum a posteriori (MAP) inference process. Moreover, we render the computation of the probability marginals used here as exact by constructing our factor graph making use of a Delaunay triangulation. We illustrate the utility of our method for pixelwise illuminant colour recovery on two widely available datasets and compare against a number of alternatives. We also show sample colour correction results on real-world images.","PeriodicalId":6539,"journal":{"name":"2015 International Joint Conference on Neural Networks (IJCNN)","volume":"27 6 1","pages":"1-8"},"PeriodicalIF":0.0000,"publicationDate":"2015-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Joint Conference on Neural Networks (IJCNN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.2015.7280811","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
This paper presents a method to recover the pixel-wise illuminant colour for scenes lit by multiple lights. Here, we start from the image formation process and pose the illuminant recovery task in hand into an evidence combining setting. To do this, we construct a factor graph making use of the scale space of the input image and a set of illuminant prototypes. The computation of these prototypes is data driven and, hence, our method is devoid of libraries or user input. The use of a factor graph allows for the illuminant estimates at different scales to be recovered making use of a maximum a posteriori (MAP) inference process. Moreover, we render the computation of the probability marginals used here as exact by constructing our factor graph making use of a Delaunay triangulation. We illustrate the utility of our method for pixelwise illuminant colour recovery on two widely available datasets and compare against a number of alternatives. We also show sample colour correction results on real-world images.