Tracking of connected components from 3D video in order to obtain tridimensional structures

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This document presents a MSc thesis focused on the development of a data integration system to generate tridimensional structures from 3D video. The work involves the extension of a recently proposed real time 3D video system. This system, composed by a video camera and a projector, obtains range images of recorded objects using slide projection of a coloured stripe pattern. This procedure allows capturing, in real time, objects´ texture and 2,5 D model, at the same time, by a technique called structured light. The data are acquired at 30 frames per second, being of high quality: the resolutions are 640 x 480 pixels and 90 x 240 points (in average), respectively. The extension that this thesis proposes aims at obtaining the tridimensional model of the objects present in a scene through data matching (texture and geometry) of various sampled frames. Thus, the current work is an intermediary step of a larger project with the intent of achieving a complete reconstruction from only a few images obtained from different viewpoints. Such reconstruction will reduce the incidence of occlusion points (very common on the original results) such that it should be possible to adapt the whole system to moving and deformable objects (In the current state, the system is robust only to static and rigid objects.). To the best of our knowledge, there is no method that has fully solved this problem. This text describes the developed work, which consists of a method to perform detection, tracking and spatial matching of connected components present in a 3D video. The video image information (texture) is combined with tridimensional sites (geometry) in order to align surface portions seen on subsequent frames. This is a key step in the 3D video that may be explored in several applications such as compression, geometric integration and scene reconstruction, to name but a few. Our approach consists of detecting salient features in both image and world spaces, for further alignment of texture and geometry. The matching process is accomplished by the application of the ICP---Iterative Closest Point---algorithm, introduced by Besl and McKay in 1992. Succesful experimental results corroborating our method are shown.

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August 17, 2007
Room 256, block A
Cidade Universitária
São Paulo
David da Silva Pires
Prof. Dr. Cesar Junior, Roberto Marcondes
Theses USP
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