Pattern Recognition Using Morphological Class Distribution Functions and Classification Trees

The paper presents an effective and robust method of classifying binary patterns. It starts with classification of foreground pixels of binary image into several spatial classes, which is performed using morphological image processing. By performing this classification with structuring elements of increasing sizes, the spatial class distribution functions are produced. These functions are normalized and sampled in order to obtain feature vectors of constant length that are invariant to pattern translation, rotation and scaling. Such feature vectors are next used to perform tree-based classification.