Sum and Difference Histograms for Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Synthesis and Analysis of Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Experiments in colour texture analysis
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Multispectral Texture Analysis Using Interplane Sum- and Difference-Histograms
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Computing illumination-invariant descriptors of spatially filtered color image regions
IEEE Transactions on Image Processing
A multiscale representation including opponent color features for texture recognition
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Perceptually uniform color spaces for color texture analysis: an empirical evaluation
IEEE Transactions on Image Processing
Multiscale color invariants based on the human visual system
IEEE Transactions on Image Processing
Content-based image retrieval of skin lesions by evolutionary feature synthesis
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
A query-by-example content-based image retrieval system of non-melanoma skin lesions
MCBR-CDS'09 Proceedings of the First MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
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Color texture algorithms have been under investigation for quite a few years now. However, the results of these algorithms are still under considerable influence of the illumination conditions under which the images were captured. It is strongly desireable to reduce the influence of illumination as much as possible to obtain stable and satisfying classification results even under difficult imaging conditions, as they can occur e.g. in medical applications like endoscopy. In this paper we present the analysis of a well-known texture analysis algorithm, namely the sum- and difference-histogram features, with respect to illumination changes. Based on this analysis, we propose a novel set of features factoring out the illumination influence from the majority of the original features. We conclude our paper with a quantitative, experimental evaluation on artificial and real image samples.