1 October 2004 Three-dimensional rigid motion estimation using genetic algorithms from an image sequence in an active stereo vision system
Albert Dipanda, Sanghyuk Woo
Author Affiliations +
Abstract
This paper proposes a method for estimating the three-dimensional (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 the objects to be analyzed by a pyramid-shaped laser beam. By associating the laser rays with the spots in the two-dimensional 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 3D 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 an optimal solution using genetic algorithms. A chromosome is encoded by concatenating seven binary coded parameters, the angle, and the three components of the rotation vector axis, and the three translation vector components. We have defined an original fitness function for calculating the similarity measure between two surfaces. Experimental results with real and synthetic image sequences are presented to show the effectiveness and the robustness of the method.
©(2004) Society of Photo-Optical Instrumentation Engineers (SPIE)
Albert Dipanda and Sanghyuk Woo "Three-dimensional rigid motion estimation using genetic algorithms from an image sequence in an active stereo vision system," Journal of Electronic Imaging 13(4), (1 October 2004). https://doi.org/10.1117/1.1789985
Published: 1 October 2004
Lens.org Logo
CITATIONS
Cited by 4 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Motion estimation

3D image processing

3D modeling

3D acquisition

3D image reconstruction

Cameras

Genetic algorithms

Back to Top