AFFINE INVARIANT DESCRIPTOR AND RECOGNITION OF 3D OBJECTS USING NEURAL NETWORKS AND PRINCIPAL COMPONENT ANALYSIS
Ahmed El Oirrak, Driss Aboutajdine · 2010
The increasing number of objects 3D are available on the Internet or in specialized databases and require the establishment of methods to develop description and recognition techniques[1,2,3] to access intelligently to the contents of these objects . In this context, our work whose objective is to present the methods of description and recognizing of 3D objects are based on neural networks and principal component analysis. In fact, it consists of determining invariant descriptors [4, 5] and recognizing the objects of a database similar to a given object (query object) using neural networks, descriptor vectors extracted from the principal component analysis and concluded equations from the same analysis and neural networks. The 3D objects of this database are transformations of 3D objects by one element of the overall transformation. The set of transformations considered in this work is the general affine group. The measure of similarity between two descriptor vector objects is achieved by a similarity function using the Euclidean distance.