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1 July 2003 Three-dimensional motion estimation using genetic algorithms from image sequence in an active stereo vision system
Albert Dipanda, Jerome Ajot, Sanghyuk Woo
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Proceedings Volume 5016, Computational Imaging; (2003) https://doi.org/10.1117/12.477361
Event: Electronic Imaging 2003, 2003, Santa Clara, CA, United States
Abstract
This paper proposes a method for estimating 3D rigid motion parameters from an image sequence of a moving object. The 3D surface measurement is achieved using an active stereovision system composed of a camera and a light projector, which illuminates objects to be analyzed by a pyramid-shaped laser beam. By associating the laser rays and the spots in the 2D image, the 3D points corresponding to these spots are reconstructed. Each image of the sequence provides a set of 3D points, which is modeled by a B-spline surface. Therefore, estimating the motion between two images of the sequence boils down to matching two B-spline surfaces. We consider the matching environment as an optimization problem and find the optimal solution using Genetic Algorithms. A chromosome is encoded by concatenating six binary coded parameters, the three angles of rotation and the x-axis, y-axis and z-axis translations. We have defined an original fitness function to calculate the similarity measure between two surfaces. The matching process is performed iteratively: the number of points to be matched grows as the process advances and results are refined until convergence. Experimental results with a real image sequence are presented to show the effectiveness of the method.
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Albert Dipanda, Jerome Ajot, and Sanghyuk Woo "Three-dimensional motion estimation using genetic algorithms from image sequence in an active stereo vision system", Proc. SPIE 5016, Computational Imaging, (1 July 2003); https://doi.org/10.1117/12.477361
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KEYWORDS
3D image processing
Motion estimation
3D modeling
3D acquisition
3D image reconstruction
Cameras
Genetic algorithms
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