3D Model Search and Retrieval Using the Spherical Trace Transform
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Research Article 3D Model Search and Retrieval Using the Spherical Trace Transform Dimitrios Zarpalas,1, 2 Petros Daras,1, 2 Apostolos Axenopoulos,1, 2 Dimitrios Tzovaras,1, 2 and Michael G. Strintzis1, 2 1 Information
Processing Laboratory, Electrical and Computer Engineering Department, Aristotle University of Thessaloniki, Thessaloniki 54006, Greece 2 Informatics and Telematics Institute, 1st km Thermi-Panorama Road, P.O.Box 361, Thermi-Thessaloniki 57001, Greece Received 31 January 2006; Accepted 22 June 2006 Recommended by Ming Ouhyoung This paper presents a novel methodology for content-based search and retrieval of 3D objects. After proper positioning of the 3D objects using translation and scaling, a set of functionals is applied to the 3D model producing a new domain of concentric spheres. In this new domain, a new set of functionals is applied, resulting in a descriptor vector which is completely rotation invariant and thus suitable for 3D model matching. Further, weights are assigned to each descriptor, so as to significantly improve the retrieval results. Experiments on two different databases of 3D objects are performed so as to evaluate the proposed method in comparison with those most commonly cited in the literature. The experimental results show that the proposed method is superior in terms of precision versus recall and can be used for 3D model search and retrieval in a highly efficient manner. Copyright © 2007 Dimitrios Zarpalas et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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INTRODUCTION
With the general availability of 3D digitizers, scanners and the technology innovation in 3D graphics and computational equipment, large collections of 3D graphical models can be readily built up for different applications [1], that is, in CAD/CAM, games design, computer animations, manufacturing, and molecular biology. For example, a high number of new 3D structures of molecules have been stored in the worldwide repository Protein Data Bank (PDB) [2], where the number of the 3D molecular structure data increases rapidly, currently exceeding 24 000. For such large databases, the method whereby 3D models are sought merits careful consideration. The simple and efficient query-by-content approach has, up to now, been almost universally adopted in the literature. Any such method, however, must first deal with the proper positioning of the 3D models. The two prevalent in the literature methods for the solution to this problem seek either: (i) pose normalization: models are first placed into a canonical coordinate frame (normalizing for translation, scaling, and rotation), then, the best measure
of similarity is found comparing the extracted feature vectors; or (ii) descriptor invariance: models are described in a transformation invariant manner, so that any transformation of a model will be described in the same way, and the best measure of sim
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