Using chromaticity distributions and eigenspace analysis for pose-, illumination-, and specularity-invariant recognition of 3D objects

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. The authors propose an approach that identifies object color distributions influenced by: (1) illumination pose, (2) illumination color and (3) specularity. They 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 eigenbasis vectors constructed from principal components analysis. Since specular reflections may alter the chromaticity distributions of rest objects, a model-based specularity detection/rejection algorithm, called chromaticity differencing, is developed to reduce these effects