Paper Info
Title
Three-Dimensional Rigid Motion Estimation Using Genetic Algorithms From An Image Sequence In An Active Stereo Vision System
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.
Year
DOI
Venue
2003
10.1117/12.477361
COMPUTATIONAL IMAGING
Keywords
Field
DocType
active stereovision, 3D reconstruction, 3D rigid motion estimation, genetic algorithms
Iterative reconstruction,Computer vision,Active vision,Motion field,Similarity measure,Stereopsis,Computer science,Fitness function,Artificial intelligence,Real image,Motion estimation
Conference
Volume
ISSN
Citations 
5016
0277-786X
2
PageRank 
References 
Authors
0.42
12
3
Name
Order
Citations
PageRank
Albert Dipanda122124.76
Jerome Ajot2516.75
sanghyuk woo320.42