Estimating Lighting Direction and Classifying Textures
Author(s) -
Mike J. Chantler,
G. McGunnigle,
Andreas Penirschke,
Μαρία Πέτρου
Publication year - 2002
Publication title -
citeseer x (the pennsylvania state university)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5244/c.16.72
Subject(s) - standard illuminant , artificial intelligence , pattern recognition (psychology) , classifier (uml) , computer science , azimuth , computer vision , probabilistic logic , image texture , mathematics , image processing , image (mathematics) , geometry
The appearance of a rough surface is affected by the direction from which it is lit and texture classifiers should account for this. We propose a classifier that is robust to lighting direction—even when the direction is unknown. An existing model of the dependency of texture features on lighting direction is used to develop a probabilistic model. Given a feature set, the algorithm estimates the most likely illumination direction for each texture class. The likelihoods of each candidate (with their estimated lighting) are compared to classify the sample. The ability of the classifier to identify illuminant direction, and to assign the correct class, was tested on 25 real texture samples. The classifier was able to accurately estimate both the azimuth and the zenith of the light source for most textures and gave a 98% classification rate.
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