Gray level processing and the recognition of embedded patterns

Pattern classes composed of multiple gray level imagery can be recognized through processing of feature sets extracted from measures of the gray level or intensity structures of the patterns. The absence of geometric dependence in the feature extraction method leads to the formation of feature sets which are invariant under the pattern perturbations of translation, rotation, and dilation. This paper considers the application of the gray level processing method to a two-class recognition problem where the object gray level patterns are embedded in a gray level structured background of unknown character. In attacking this problem, a preprocessing algorithm is established which dissects the pattern image into a number of subsets of connected regions as a function of gray level. A "one at a time" sequential minimization procedure is then employed to determine an optimal gray level subset based on a minimum distance criterion.