CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 1996 vol.18 Issue No.02 - February
Issue No.02 - February (1996 vol.18)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.481544
<p><b>Abstract</b>—Traditional approaches to three dimensional object recognition exploit the relationship between three dimensional object geometry and two dimensional image geometry. The capability of object recognition systems can be improved by also incorporating information about the color of object surfaces. Using physical models for image formation, we derive invariants of local color pixel distributions that are independent of viewpoint and the configuration, intensity, and spectral content of the scene illumination. These invariants capture information about the distribution of spectral reflectance which is intrinsic to a surface and thereby provide substantial discriminatory power for identifying a wide range of surfaces including many textured surfaces. These invariants can be computed efficiently from color image regions without requiring any form of segmentation. We have implemented an object recognition system that indexes into a database of models using the invariants and that uses associated geometric information for hypothesis verification and pose estimation. The approach to recognition is based on the computation of local invariants and is therefore relatively insensitive to occlusion. We present several examples demonstrating the system's ability to recognize model objects in cluttered scenes independent of object configuration and scene illumination. The discriminatory power of the invariants has been demonstrated by the system's ability to process a large set of regions over complex scenes without generating false hypotheses.</p>
Object recognition, color, color vision, color constancy, illumination invariant, machine vision, illumination correction.
David Slater, Glenn Healey, "The Illumination-Invariant Recognition of 3D Objects Using Local Color Invariants", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.18, no. 2, pp. 206-210, February 1996, doi:10.1109/34.481544