2 Centre de Mathématiques et de Leurs Applications

Abstract. Shape recognition methods are often based on feature com-parison. When features are of different natures, combining the value of distances or (dis-)similarity measures is not easy since each feature has its own amount of variability. Statistical models are therefore needed. This article proposes a statistical method, namely an a contrario method, to merge features derived from several families of size functions. This merg-ing is usually achieved through a touchy normalizing of the distances. The proposed model consists in building a probability measure. It leads to a global shape recognition method dedicated to perceptual similarities. 1 Global Shape Recognition 1.1 A Brief State of the Art Shape recognition is a central problem in computer vision. Geometrical shapes can be defined as outlines of objects and are thus formally connected bounded domains of the plane. Many shape recognition methods are global in the sense that the extracted features are computed over the whole solid shape. Since the