Using a spectral reflectance model for the illumination-invariantrecognition of local image structure
Slater, D.
Healey, G.
Comput. Vision Lab., California Univ., Irvine, CA ;
Abstract
We represent local spatial structure in a color image using
feature matrices that are computed from an image region. Feature
matrices contain significantly more information about local image
structure than previous representations. Although feature matrices are
useful for surface recognition, this representation depends on the
spectral properties of the scene illumination. Using a finite
dimensional linear model for surface spectral reflectance with the same
number of parameters as the number of color bands, we show that
illumination changes correspond to linear transformations of the feature
matrices and that surface rotations correspond to circular shifts of the
matrices. From these relationships we derive an algorithm for
illumination and geometry invariant recognition of local surface
structure. We demonstrate the algorithm with a series of experiments on
images of real objects
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
