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| David Slater, Glenn Healey, "The Illumination-Invariant Matching of Deterministic Local Structure in Color Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 10, pp. 1146-1151, October, 1997. | |||
| BibTex | x | ||
| @article{ 10.1109/34.625119, author = {David Slater and Glenn Healey}, title = {The Illumination-Invariant Matching of Deterministic Local Structure in Color Images}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {19}, number = {10}, issn = {0162-8828}, year = {1997}, pages = {1146-1151}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.625119}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Pattern Analysis and Machine Intelligence TI - The Illumination-Invariant Matching of Deterministic Local Structure in Color Images IS - 10 SN - 0162-8828 SP1146 EP1151 EPD - 1146-1151 A1 - David Slater, A1 - Glenn Healey, PY - 1997 KW - Computer vision KW - machine vision KW - color KW - color vision KW - color constancy KW - recognition KW - invariant recognition KW - local methods. VL - 19 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER - | |||
Abstract—The availability of multiple spectral measurements at each pixel in an image provides important additional information for recognition. Spectral information is of particular importance for applications where spatial information is limited. Such applications include the recognition of small objects or the recognition of small features on partially occluded objects. We introduce a feature matrix representation for deterministic local structure in color images. Although feature matrices are useful for recognition, this representation depends on the spectral properties of the scene illumination. Using a linear model for surface spectral reflectance with the same number of parameters as the number of color bands, we show that changes in the spectral content of the illumination correspond to linear transformations of the feature matrices, and that image plane rotations correspond to circular shifts of the matrices. From these relationships, we derive an algorithm for the recognition of local surface structure which is invariant to these scene transformations. We demonstrate the algorithm with a series of experiments on images of real objects.
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