Color Invariant Descriptors and Color Correction Based on Illuminant Estimation
Date Issued
2011
Date
2011
Author(s)
Chang, Feng-Ju
Abstract
Photometric invariance or we say color invariance is important for many applications such as image retrieval, image segmentation, image classification, object and scene recognition, etc. Since both color based feature extraction and detection are susceptive to the varying imaging conditions including light source as well as the surface geometry, our aim is to obtain the color descriptor or feature which is invariant with respect to the photometric variation and only depends on the object reflectance.
According to the color image formation model, we can acquire numerous full color invariant descriptors under a certain illuminant as well as the surface assumption. The quasi-invariants, derived by subtracted the projected variants from the color derivative are more stable than full invariants to remove shadow-shading and highlight edges and therefore suitable for feature detection. As to the feature extraction, the full invariants are still needed since the quasi-invariants are in fact related to the illuminant from the mathematical deduction. With the weights by measured uncertainties as well as the structure (color) tensor, the full invariants become more robust in feature extraction.
If we can represent a pixel in the illuminant invariant direction, the 1D shadow free image can be obtained. The 2D illuminant invariant image can be further acquired by multiplied with the projection matrix. As to the 3D shadow free image, we propose to use a soft classification based method, fuzzy C means, to effectively and accurately segment the shadow area and then detect the shadow edges. According to the segmentation map, we can further adjust the brightness as well as the texture of the shadow area in order to get better recovered result. Finally, we utilize the modified intensity values as well as the gradients to solve the Poisson equation by discrete sine transform to obtain a 3D shadow-free image. Moreover, we propose to use the exemplar-based inpainting technique to make the reconstructed pixels which are near shadow edges look more natural.
In addition to the representation of the color invariant descriptors for reaching color constancy, illuminant estimation and color correction is another essential branch for color constancy. Considering the bad impulse noise resistance of the White Patch Retinex (WPR), susceptibility to large uniformly colored regions of the Gray world (GW), non-generality of the weighted Gray Edge (WGE) method, we propose an illuminant estimation method by adaptive scanning of accumulative color histograms based on the color cast analysis in color space for improving WPR. Also, we use the color tensor to assign more weight to regions with larger color variation for improving GW. As to WGE, the initial guess of the illuminant from original Gray Edge hypothesis, eigen-value sum of the structure tensors with shadow-shading as well as specular variants, and singular value decomposition are considered to make it more generalized. After improving all above single algorithms, we further propose an illuminant fusion scheme which is called fuzzy frequency ratio (FFR) in that it is not reasonable for best frequency ratio (BFR) to only consider the best illuminant (hard decision). That is, we take all above improved illuminant estimation measures into account and assign distinct weights to them. Finally, FFR is integrated to the regularized local regression (RLR-FFR). From our experiments, the improved single algorithms are all indeed better than original methods and the proposed combination scheme (FFR) is better than BFR and also more proper to be used for setting the initial weights in the regularized local regression.
Subjects
Color invariance
Quasi-invariants
Color tensor
Illuminant estimation
Color correction
Type
thesis
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