Object recognition using a multi-spectral imaging system

A spectral-imaging system and algorithms for identifying objects in a natural scene based on surface-spectral reflectances are described. The Imaging system is composed of a liquid-crystal tunable filter, a monochrome CCD camera, and a personal computer. The tunable filter is convenient for spectral imaging because the wavelength band can be changed easily and electronically. It is described how we can recover the surface-spectral reflectances of natural objects by using the multi-spectral imaging system. Algorithms are presented for estimating both spectral functions of the illuminant spectral-power distribution and surface-spectral reflectance from the spectral image data. Moreover, effective image processing procedures are proposed for highlight extraction and region segmentation. The segmentation is based on a pixel classification method using only the maximum sensor outputs. The overall performance of the proposed imaging system and algorithms is examined in an experiment using natural products, in which 21 spectral images are acquired in the wavelength range of 450-650 nm.