Abstract

Detection followed by morphological processing is commonly used in machine vision. However, choosing the morphological operators and parameters is often done in a heuristic manner since a statistical characterization of their per- formance is not easily derivable. If we consider a morphology operator sequence as a classifier distinguishing between two patterns, the automatic choice of the operator sequence and parameters is possible if one derives the misclassification distribution as a function of the input signal distributions, the operator sequence, and parameter choices. The main essence of this paper is the illustration that mis- classification statistics, the distribution of bit errors measured by the Hamming distance, can be computed by using an embeddable Markov chain approach. Li- cense plate extraction is used as a case study to illustrate the utility of the theory on real data.