The Bright-chromagenic Algorithm for Illuminant Estimation

In this paper, we propose a new algorithm for illumi-nant estimation. We begin by reviewing the concept of chro-magenic colour constancy, where two pictures are taken from each scene: a normal one and one where a coloured filter is placed in front of the camera, and look at param-eters known to affect its performance such as filters and sensor choice. We show that the basic formulation of the chromagenic algorithm has inherent weaknesses: a need for perfectly reg-istered images and occasional large errors in illuminant es-timation. Our first contribution is to analyse the algorithm performance with respect to the reflectances present in a scene and demonstrate that fairly bright and desaturated reflectances (e.g., achromatic and pastel colours) provide significantly better chromagenic illuminant estimation. This analysis leads to the bright-chromagenic algo-rithm. We show that it not only remedies the large error problem but also allows us to relax the image registration constraint. Experiments performed on a variety of syn-thetic and real data show that the newly designed bright-chromagenic algorithm significantly -in a strict statistical sense- outperforms current illuminant estimation methods, including those having a substantially higher complexity

Fast Re-integration of Shadow Free Images

In imaging applications, computation is often carried out in a derivative (gradient) domain. For example, we can at-tenuate small image differences by thresholding the gradi-ent and then reintegrate. Unfortunately, the reintegration is an expensive task. Reintegration is often carried out in 2D (usually using 2D Fourier transform) or through multiple 1D paths as in Retinex. In this paper, we show that using a small number of non-random paths, each of which is a tour the size of the image, is an effective and fast method for reintegration. We apply our method to the problem of reintegrating a shadow free gradient derivative image. Results are com-petitive with those obtained using 2D methods. Yet, the reintegration presented here is an order of magnitude quicker

Path-based shadow removal

It has been shown that by thresholding the image gradient at the location of shadow edges and then reintegrating, shadow-free images can be obtained. Unfortunately, the current methods are computationally expensive and also create artifacts in the reintegrated image. Our proposed method uses non-intersecting random paths (also called Hamiltonian paths) to allow for fast 1D reintegration. Because the artifacts are due to missing gradient information, we further improve the results by inpainting the detected shadow edges as to prevent the occurrence of unwanted artifacts

Material Classification Using Color and NIR Images

Material classification is becoming more important in computer vision and digital photography applications, which require accurate classification of objects present in the imaged scene. This is a very challenging task because the sheer diversity of scene content and lighting conditions decreases the usefulness of many color- and texture-based features used in image classification. In this work, we investigate the potential offered by using information outside of the visible spectrum, specifically the near-infrared (NIR). The difference in the NIR images ’ intensities is not just due to the particular color of the material, but also absorption and reflectance characteristics of the colorant. This relative independency of NIR and color information makes NIR images a prime candidate for classification. The database, on which the training and testing were conducted, consists of textile, tile, linoleum and wood samples. To classify the materials, visible and NIR images were analyzed according to their lightness, texture, and color. The analysis results were the input to a classifier in form of feature vectors. The results show that our database is classified almost exactly. Comparing with visible-only features, wood and textile samples were better classified due to the additional information the NIR images provide

Detecting Illumination in images

In this paper we present a surprisingly simple yet powerful method for detecting illumination determining which pixels are lit by different lights in images. Our method is based on the chromagenic camera, which takes two pictures of each scene: one is captured as normal and the other through a coloured filter. Previous research has shown that the relationship between the colours, the RGBs, in the filtered and unfiltered images depends strongly on the colour of the light and this can be used to estimate the colour of the illuminant. While chromagenic illuminant estimation often works well it can and does fail and so is not itself a direct solution to the illuminant detection problem. In this paper we dispense with the goal of illumination estimation and seek only to use the chromagenic effect to find out which parts of a scene are illuminated by the same lights. The simplest implementation of our idea involves a combinatorial search. We precompute a dictionary of possible illuminant relations that might map RGBs to filtered counterparts from which we select a small number m corresponding to the number of distinct lights we think might be present. Each pixel, or region, is assigned the relation from this m-set that best maps filtered to unfiltered RGB. All m-sets are tried in turn and the one that has the minimum prediction error over all is found. At the end of this search process each pixel or region is assigned an integer between 1 and m indicating which of the m lights are thought to have illuminated the region. Our simple search algorithm is possible when m = 2 (and m = 3) and for this case we present experiments that show our method does a remarkable job in detecting illumination in images: if the 2 lights are shadow and non- shadow, we find the shadows almost effortlessly. Compared to ground truth data, our method delivers close to optimal performance