International Journal of Computer Vision
Image matching using run-length feature
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
Is Machine Colour Constancy Good Enough?
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
A new approach to color person image indexing and retrieval
Machine Graphics & Vision International Journal - Special issue on latest results in colour image processing and applications
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Image retrieval system based on color-complexity and color-spatial features
Journal of Systems and Software
Color histograms adapted to query-target images for object recognition across illumination changes
EURASIP Journal on Applied Signal Processing
Novelty detection in wildlife scenes through semantic context modelling
Pattern Recognition
Object recognition using Gabor co-occurrence similarity
Pattern Recognition
Color texture analysis using CFA chromatic co-occurrence matrices
Computer Vision and Image Understanding
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One of the most widely used approaches in the context of object recognition across illumination changes consists in comparing the images by means of the intersection between invariant histograms. However, this approach does not provide satisfying results with limited image databases. We propose to cope with the problem of illumination changes by analyzing simultaneously the color components of the pixels and their spatial arrangement in the image. For this purpose, we introduce the chromatic co-occurrence matrices to characterize the relationship between the color component levels of neighboring pixels. In order to compare two images acquired under different illuminations, these matrices are transformed into adapted co-occurrence matrices that are determined so that their intersection is higher when the two images contain the same object lit with different illuminations than when they contain different objects.