Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Simple Strategy for Calibrating the Geometry of Light Sources
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Objects Using Color-Annotated Adjacency Graphs
Shape, Contour and Grouping in Computer Vision
Illumination Invariant Recognition of Color Texture Using Correlation and Covariance Functions
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Defect detection in textured surfaces using color ring-projection correlation
Machine Vision and Applications
The evaluation of normalized cross correlations for defect detection
Pattern Recognition Letters
Local relational string and mutual matching for image retrieval
Information Processing and Management: an International Journal
Affine illumination compensation for multispectral images
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Illumination invariant color texture analysis based on sum- and difference-histograms
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
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Spatial filters provide a useful and efficient means of analyzing an input color image into components that capture different spatial properties. Representations based on spatial filtering have restricted usefulness for recognition, however, because the output of a spatial filter across an image depends on the scene illumination conditions. We use a physically accurate linear model for spectral reflectance to derive invariants of distributions in spatially filtered color images that do not depend on the scene illumination. These invariants can be used for the illumination-invariant recognition of regions following an arbitrary linear filtering operation. We describe a method for illumination correction based on color distributions and introduce an illumination change consistency constraint that is useful for verifying matches obtained using the invariants. We show, using a set of classification experiments, that the filtered distribution invariants can significantly improve the capability of a recognition system in environments where illumination cannot be controlled