Classifying Surface Texture while Simultaneously Estimating Illumination Direction

Authors:
M. Chantler;M. Petrou;A. Penirsche;M. Schmidt;G. McGunnigle
Affiliations:
School of Mathematical and Computer Science, Heriot-Watt University, Riccarton, Great Britain;Department of Electronic Engineering, University of Surrey, Guildford, UK;School of Mathematical and Computer Science, Heriot-Watt University, Riccarton, Great Britain;School of Mathematical and Computer Science, Heriot-Watt University, Riccarton, Great Britain;School of Mathematical and Computer Science, Heriot-Watt University, Riccarton, Great Britain
Venue:
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Year:
2005

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Abstract

We propose a novel classifier that both classifies surface texture and simultaneously estimates the unknown illumination conditions. A new formal model of the dependency of texture features on lighting direction is developed which shows that their mean vectors are trigonometric functions of the illuminations' tilt and slant angles. This is used to develop a probabilistic description of feature behaviour which forms the basis of the new classifier. Given a feature set from an image of an unknown texture captured under unknown illumination conditions the algorithm first estimates the most likely illumination direction for each possible texture class. These estimates are used to calculate the class likelihoods and the classification is made accordingly.The ability of the classifier to estimate illuminant direction, and to assign the correct class, was tested on 55 real texture samples in two stages. The classifier was able to accurately estimate both the tilt and the slant angles of the light source for the majority of textures and gave a 98% classification rate.