3D Recognition Using Neural Networks

With the advent of the Internet, exchanges and the acquisition of information, description and recognition of 3D objects have been as extensive and have become very important in several domains, which require the establishment of methods to develop description and recognition techniques to access intelligently to the contents of these objects. This paper deals for 3D models recognition. Thus under general affine transform we propose an approach based on neural network. The recognition is done by measuring the similarity between a sample of object and its transformed obtained by parameters extracted from neural networks using the euclidean distance.

[1]  Ming Ouhyoung,et al.  On Visual Similarity Based 3D Model Retrieval , 2003, Comput. Graph. Forum.

[2]  Ilias Maglogiannis,et al.  An integrated computer supported acquisition, handling, and characterization system for pigmented skin lesions in dermatological images , 2005, IEEE Transactions on Information Technology in Biomedicine.

[3]  Marc Rioux,et al.  Nefertiti: a query by content software for three-dimensional models databases management , 1997, Proceedings. International Conference on Recent Advances in 3-D Digital Imaging and Modeling (Cat. No.97TB100134).

[4]  Mohamed Daoudi,et al.  A Bayesian 3-D Search Engine Using Adaptive Views Clustering , 2007, IEEE Transactions on Multimedia.

[5]  Ahmed El Oirrak,et al.  A 3D search engine based on 3D curve analysis , 2010, Signal Image Video Process..

[6]  Dietmar Saupe,et al.  3D Model Retrieval with Spherical Harmonics and Moments , 2001, DAGM-Symposium.

[7]  Michel Verleysen,et al.  Les Réseaux de Neurones Artificiels , 1996 .

[8]  Bernard Chazelle,et al.  Matching 3D models with shape distributions , 2001, Proceedings International Conference on Shape Modeling and Applications.

[9]  R. Lepage,et al.  Les réseaux de neurones artificiels et leurs applications en imagerie et en vision par ordinateur , 2003 .

[10]  Scott E. Fahlman,et al.  An empirical study of learning speed in back-propagation networks , 1988 .