alexa 3D Modeling with Support Vector Machines
ISSN: 2165- 7866

Journal of Information Technology & Software Engineering
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Research Article

3D Modeling with Support Vector Machines

Montilla G1, Bosnjak A1*, Paluszny M2 and Villegas H1

1The Image Processing Center Universidad de Carabobo, Valencia, Venezuela

2Graphical Computer and Applied Geometry Center, Universidad Central de Venezuela, Caracas, Venezuela

*Corresponding Author:
Bosnjak A
The Image Processing Center Universidad de Carabobo
Valencia, Venezuela
E-mail: [email protected]

Received Date: December 17, 2011; Accepted Date: May 19, 2012; Published Date: May 21, 2012

Citation: Montilla G, Bosnjak A, Paluszny M, Villegas H (2012) 3D Modeling with Support Vector Machines. J Inform Tech Softw Eng 2:110. doi:10.4172/2165-7866.1000110

Copyright: © 2012 Montilla G, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



We developed a new approach based on Support Vector Machines (SVM) to model solids defined by a set of points on their surface. In problems of classification, regression and support of distribution, SVM are focused in the hyper-planes of maximum margin in the feature space. However, the forms that could be described by these surfaces when they return to the input space have not been studied in depth. In this paper, these surfaces are used to model complex objects, connected or non- connected, with a great amount of points, in order of tens of thousands, and with various topologies (hollow, branches, etc.). Two constrains were kept: 1) The use of traditional algorithms of SVM theory; and 2) The design of the appropriate training sets from the object. This combination produced a novel tool and the results obtained illustrated the potential of the proposed method. Therefore, this new application of SVM of Vapnik is capable of creating surfaces of decision and estimation functions, which are well fitted to objects of complex topology.

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