IEEE Trans Image Process - Color constancy using 3D scene geometry derived from a single image.

Tópicos

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Resumo

The aim of color constancy is to remove the effect of the color of the light source. As color constancy is inherently an ill-posed problem, most of the existing color constancy algorithms are based on specific imaging assumptions (e.g., gray-world and white patch assumption). In this paper, 3D geometry models are used to determine which color constancy method to use for the different geometrical regions (depth/layer) found in images. The aim is to classify images into stages (rough 3D geometry models). According to stage models, images are divided into stage regions using hard and soft segmentation. After that, the best color constancy methods are selected for each geometry depth. To this end, we propose a method to combine color constancy algorithms by investigating the relation between depth, local image statistics, and color constancy. Image statistics are then exploited per depth to select the proper color constancy method. Our approach opens the possibility to estimate multiple illuminations by distinguishing nearby light source from distant illuminations. Experiments on state-of-the-art data sets show that the proposed algorithm outperforms state-of-the-art single color constancy algorithms with an improvement of almost 50% of median angular error. When using a perfect classifier (i.e, all of the test images are correctly classified into stages); the performance of the proposed method achieves an improvement of 52% of the median angular error compared with the best-performing single color constancy algorithm.

Resumo Limpo

aim color constanc remov effect color light sourc color constanc inher illpos problem exist color constanc algorithm base specif imag assumpt eg grayworld white patch assumpt paper d geometri model use determin color constanc method use differ geometr region depthlay found imag aim classifi imag stage rough d geometri model accord stage model imag divid stage region use hard soft segment best color constanc method select geometri depth end propos method combin color constanc algorithm investig relat depth local imag statist color constanc imag statist exploit per depth select proper color constanc method approach open possibl estim multipl illumin distinguish nearbi light sourc distant illumin experi stateoftheart data set show propos algorithm outperform stateoftheart singl color constanc algorithm improv almost median angular error use perfect classifi ie test imag correct classifi stage perform propos method achiev improv median angular error compar bestperform singl color constanc algorithm

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