Using a Spectral Reflectance Model for the Illumination-Invariant Recognition of Local Image Structure
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.
Cite
Text
Slater and Healey. "Using a Spectral Reflectance Model for the Illumination-Invariant Recognition of Local Image Structure." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1996. doi:10.1109/CVPR.1996.517159Markdown
[Slater and Healey. "Using a Spectral Reflectance Model for the Illumination-Invariant Recognition of Local Image Structure." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1996.](https://mlanthology.org/cvpr/1996/slater1996cvpr-using/) doi:10.1109/CVPR.1996.517159BibTeX
@inproceedings{slater1996cvpr-using,
title = {{Using a Spectral Reflectance Model for the Illumination-Invariant Recognition of Local Image Structure}},
author = {Slater, David and Healey, Glenn},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {1996},
pages = {770-775},
doi = {10.1109/CVPR.1996.517159},
url = {https://mlanthology.org/cvpr/1996/slater1996cvpr-using/}
}