Combining range and intensity data with a hidden Markov model

Abstract

The paper treats the analysis of an industrial inspection problem, namely the segmentation and discrimination of similar-looking bottles based on a multispectral image consisting of both range and intensity data. A contextual pixel classification is performed using a whole line as neighborhood. The framework of hidden Markov models together with a fast algorithm from control engineering makes this possible. The method is compared to J. Haslett's method (1985) for contextual classification, and performs significantly better.<>