Using moment invariants to analyze 3-D color textures

We introduce an image model for color texture based on spatial correlation functions defined both within and between color bands. We show that three dimensional geometric transformations of a surface in the scene produce corresponding transformations in these correlation functions. From this analysis, we derive invariants of color correlation functions that can be computed efficiently and used for geometry invariant recognition. We show experimentally that these invariants are effective for recognition in situations where neither color distributions nor gray scale texture are sufficient. Following recognition, the estimated color correlation functions can be used to recover the three dimensional location and orientation of surfaces. The color texture invariants can be used in the recognition of three dimensional objects, the segmentation of images of three dimensional scenes, and the searching of image databases