This tutorial presents an introduction to the studies undertaken by the authors and their collaborators between 1997 and 2009 on the topic of dissimilarity representations for pattern recognition.

Properties of shapes and shape patterns are investigated in order to represent shape pattern knowledge for supporting shape pattern recognition. It is based on the notion that shape patterns are classified in terms of similarity of spatial relationships as well as physical properties. Methods for shape pattern recognition are explained and examples from an implementation are presented.

We propose an innovative segmentation algorithm based on mathematical morphology operators. This definition is based on a morphological and fuzzy pattern-matching approach, and consists in comparing an object to a fuzzy landscape representing the degree of satisfaction of an affinity relationship. It has good formal properties, it is flexible, it fits the intuition, and it can be used for struc...

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In order to cope with the ambiguity of spatial relative position concepts, we propose a new definition of the relative position between two objects in a fuzzy set framework. This definition is based on a morphological and fuzzy pattern-matching approach, and consists of comparing an object to a fuzzy landscape representing the degree of satisfaction of a directional relationship to a reference ...