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Implementation techniques for geometric branch-and-bound matching methods.
- Source :
- Computer Vision & Image Understanding; Jun2003, Vol. 90 Issue 3, p258, 37p
- Publication Year :
- 2003
-
Abstract
- Algorithms for geometric matching and feature extraction that work by recursively subdividing transformation space and bounding the quality of match have been proposed in a number of different contexts and become increasingly popular over the last few years. This paper describes matchlist-based branch-and-bound techniques and presents a number of new applications of branch-and-bound methods, among them, a method for globally optimal partial line segment matching under bounded or Gaussian error, point matching under a Gaussian error model with subpixel accuracy and precise orientation models, and a simple and robust technique for finding multiple distinct object instances. It also contains extensive reference information for the implementation of such matching methods under a wide variety of error bounds and transformations. In addition, the paper contains a number of benchmarks and evaluations that provide new information about the runtime behavior of branch-and-bound matching algorithms in general, and that help choose among different implementation strategies, such as the use of point location data structures and space/time tradeoffs involving depth-first search. [Copyright &y& Elsevier]
- Subjects :
- BRANCH & bound algorithms
GEOMETRY
MATHEMATICS
Subjects
Details
- Language :
- English
- ISSN :
- 10773142
- Volume :
- 90
- Issue :
- 3
- Database :
- Supplemental Index
- Journal :
- Computer Vision & Image Understanding
- Publication Type :
- Academic Journal
- Accession number :
- 10234599
- Full Text :
- https://doi.org/10.1016/S1077-3142(03)00026-2