Recognizing Objects Using Color-Annotated Adjacency Graphs
Shape, Contour and Grouping in Computer Vision
Spectro-Spatial Gradients for Color-Based Object Recognition and Indexing
CAIP '99 Proceedings of the 8th International Conference on Computer Analysis of Images and Patterns
Illuminant and gamma comprehensive normalisation in log RGB space
Pattern Recognition Letters - Special issue: Colour image processing and analysis
Spectral gradients for color-based object recognition and indexing
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
Specularity detection using time-of-flight cameras
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Colour indexing across illumination
IM'99 Proceedings of the 1999 international conference on Challenge of Image Retrieval
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The distribution of object colors can be effectively utilized for recognition and indexing. Difficulties arise in the recognition of object color distributions when there are variations in illumination color, changes in object pose with respect to illumination direction, and specular reflections. However, most of the recent approaches to color-based recognition focus mainly on illumination color invariance. We propose an approach that identifies object color distributions influenced by: (1) illumination pose, (2) illumination color and (3) specularity. We suggest the use of chromaticity distributions to achieve illumination pose invariance. To characterize changes in chromaticity distribution due to illumination color, a set of chromaticity histograms of each object is generated for a range of lighting colors based on linear models of illumination and reflectance, and the histograms are represented using a small number of eigen basis vectors constructed from principal components analysis. Since specular reflections may alter the chromaticity distributions of test objects, a model-based specularity detection/rejection algorithm, called chromaticity differencing, is developed to reduce these effects.