Model-based image matching using location
Model-based image matching using location
Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the use of interval arithmetic in geometric branch and bound algorithms
Pattern Recognition Letters
Implementation techniques for geometric branch-and-bound matching methods
Computer Vision and Image Understanding
Matching by Linear Programming and Successive Convexification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonmyopic sensor scheduling and its efficient implementation for target tracking applications
EURASIP Journal on Applied Signal Processing
A branch & bound algorithm for medical image registration
IWCIA'08 Proceedings of the 12th international conference on Combinatorial image analysis
Globally Optimal Algorithms for Stratified Autocalibration
International Journal of Computer Vision
Fast PRISM: Branch and Bound Hough Transform for Object Class Detection
International Journal of Computer Vision
Linear programming matching and appearance-adaptive object tracking
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Branch&Rank for Efficient Object Detection
International Journal of Computer Vision
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Over the last decade, a number of methods for geometric matching based on a branch-and-bound approach have been proposed. Such algorithms work by recursively subdividing transformation space and bounding the quality of match over each subdivision. No direct comparison of the major implementation strategies has been made so far, so it has been unclear what the relative performance of the different approaches is. This paper examines experimentally the relative performance of different implementation choices in the implementation of branch-and-bound algorithms for geometric matching: alternatives for the computation of upper bounds across a collection of features, and alternatives the order in which search nodes are expanded. Two major approaches to computing the bounds have been proposed: the matchlist based approach, and approaches based on point location data structures. A second issue that is addressed in the paper is the question of search strategy; branch-and-bound algorithms traditionally use a "best-first" search strategy, but a "depth-first" strategy is a plausible alternative. These alternative implementations are compared on an easily reproducible and commonly used class of test problems, a statistical model of feature distributions and matching within the COIL-20 image database. The experimental results show that matchlist based approaches outperform point location based approaches on common tasks. The paper also shows that a depth-first approach to matching results in a 50-200 fold reduction in memory usage with only a small increase in running time. Since matchlist-based approaches are significantly easier to implement and can easily cope with a much wider variety of feature types and error bounds that point location based approaches, they should probably the primary implementation strategy for branch-and-bound based methods for geometric matching.