Catálogo de publicaciones - libros

This paper presents an application of several definitions of a mean set for use in footwear design. For a given size, footprint pressure images corresponding to different individuals constitute our raw data. Appropriate footwear design needs to have knowledge of some kind of typical footprint. Former methods based on contour relevant points are highly sensitive to contour noise; moreover, they lack repeatability because of the need for the intervention of human designers. The method proposed in this paper is based on using mean sets on the thresholded images of the pressure footprints. Three definitions are used, two of them from Vorob’ev and Baddeley-Molchanov and one morphological mean proposed by the authors. Results show that the use of mean sets improves previous methodologies in terms of robustness and repeatability.

A multidisciplinary methodology that goes from the extraction of features till the classification of a set of different granites is presented in this paper. The set of tools to extract the features that characterise the polished surfaces of granites is mainly based on mathematical morphology. The classification methodology is based on a genetic algorithm capable of searching for the input feature space used by the nearest neighbour rule classifier. Results show that is adequate to perform feature reduction and simultaneously improve the recognition rate. Moreover, the present methodology represents a robust strategy to understand the proper nature of the textures studied and their discriminant features.

Mathematical morphology tools have already been applied to a large range of application domains: from 2d grey-level image processing to colour movies and 3D medical image processing. However, they seem to have been seldom used to process 3D seismic images. The specific layer structure of these data makes them very interesting to study. This paper presents the first results we have obtained by carrying out two kinds of hierarchal segmentation tests of 3D seismic data. First, we have performed a marker based segmentation of a seismic amplitude cube constrained by a picked surface called seismic horizon. The second test has consisted in applying a hierarchical segmentation to the same seismic amplitude cube, but this time with no information about the image structure.