SPECTRAL SENSITIVITY DESIGN FOR OPTICAL SENSORS

Abstract

Artificial Color is the use of technology to emulate the basic means by which animals acquire and use spectral information. It begins with sensing of the scene using multiple broad and overlapping spectral sensitivity functions analogous with cone cells in the human visual system. In this paper we develop an algorithm to obtain a set of near-optimal application-specific spectral sensitivity functions for Artificial Color systems. The user-designed sensitivity functions result in concurrent good intra-class packing and interclass integrity in the color space for reflectance classes with known statistics. This method leads to improved color discrimination and results in more robust spectral classifiers.