Designing illuminant spectral power distribution for surface classification - Sample Data

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 first approach, a supervised framework, we formulate a biconvex optimization problem where we alternate between optimizing support vector classifier weights and optimal illuminants. In the second approach, an unsupervised dimensionality reduction, we describe and apply a new sparse Principal Component Analysis (PCA) algorithm. 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.

This repository contains sample image data used in the analysis and classification results.