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header for SPIE use Automatic reconstruction of large 3D models of real environments from

Summary: header for SPIE use
Automatic reconstruction of large 3D models of real environments from
unregistered data-sets
Faysal Boughorbal*a,b
, David L. Pageb
, Mongi A. Abidib
Ecole Nationale des Ingenieurs de Tunis, Tunisia
Imaging, Robotics, and Intelligent Systems Labortory, University of Tennessee, Electrical Engineering,
Ferris Hall, Knoxville, Tennessee, 37996-2100, USA
Towards photo-realistic 3D scene reconstruction from range and color images, we present a statistical technique for multi-
modal image registration. Statistical tools are employed to measure the dependence of two images, considered as random
distributions of pixels, and to find the pose of one imaging system relative to the other. The similarity metrics used in our
automatic registration algorithm are based on the chi-squared measure of dependence, which is presented as an alternative to
the standard mutual information criterion. These two criteria belong to the class of information-theoretic similarity measures
that quantify the dependence in terms of information provided by one image about the other. This approach requires the use
of a robust optimization scheme for the maximization of the similarity measure. To achieve accurate results, we investigated
the use of heuristics such as genetic algorithms. The retrieved pose parameters are used to generate a texture map from the


Source: Abidi, Mongi A. - Department of Electrical and Computer Engineering, University of Tennessee


Collections: Computer Technologies and Information Sciences