Designing Illuminant Spectral Power Distributions for Surface Classification

Henryk Blasinski, Joyce Farrell, Brian Wandell; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 2164-2173

Abstract


There are many scientific, medical and industrial imaging applications where users have full control of the scene illumination and color reproduction is not the primary objective For example, it is possible to co-design sensors and spectral illumination in order to classify and detect changes in biological tissues, organic and inorganic materials, and object surface properties. In this paper, we propose two different approaches to illuminant spectrum selection for surface classification. In the supervised framework we formulate a biconvex optimization problem where we alternate between optimizing support vector classifier weights and optimal illuminants. We also describe a sparse Principal Component Analysis (PCA) dimensionality reduction approach that can be used with unlabeled data. We efficiently solve the non-convex PCA problem using a convex relaxation and Alternating Direction Method of Multipliers (ADMM). We compare the classification accuracy of a monochrome imaging sensor with optimized illuminants to the classification accuracy of conventional RGB cameras with natural broadband illumination.

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[bibtex]
@InProceedings{Blasinski_2017_CVPR,
author = {Blasinski, Henryk and Farrell, Joyce and Wandell, Brian},
title = {Designing Illuminant Spectral Power Distributions for Surface Classification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}