Performance characterization of image understanding algorithms
Performance characterization of image understanding algorithms
Optimal 2D model matching using a messy genetic algorithm
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Journal of Intelligent and Robotic Systems
A System to Navigate a Robot into a Ship Structure
ICVS '01 Proceedings of the Second International Workshop on Computer Vision Systems
Optimal Threshold Estimation Using Prototype Selection
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Interpreting Sloppy Stick Figures with Constraint-Based Subgraph Matching
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Machine Vision and Applications - Special issue: IEEE WACV
Implementation techniques for geometric branch-and-bound matching methods
Computer Vision and Image Understanding
Directional edge tracking for line extraction
SPPRA'06 Proceedings of the 24th IASTED international conference on Signal processing, pattern recognition, and applications
An algorithm for projective point matching in the presence of spurious points
Pattern Recognition
Journal of Intelligent and Robotic Systems
Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping
Robotics and Autonomous Systems
Proceedings of the 7th International Conference on Mobile and Ubiquitous Multimedia
Comparing random starts local search with key feature matching
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Many-to-many matching of scale-space feature hierarchies using metric embedding
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Many-to-many graph matching via metric embedding
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Object detection by contour segment networks
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Tracking the untrackable: how to track when your object is featureless
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Contextual Geospatial Picture Understanding, Management and Visualization
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
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Local search is a well established and highly effective method for solving complex combinatorial optimization problems. Here, local search is adapted to solve difficult geometric matching problems. Matching is posed as the problem of finding the optimal many-to-many correspondence mapping between a line segment model and image line segments. Image data is assumed to be fragmented, noisy, and cluttered. The algorithms presented have been used for robot navigation, photo interpretation, and scene understanding. This paper explores how local search performs as model complexity increases, image clutter increases, and additional model instances are added to the image data. Expected run-times to find optimal matches with 95 percent confidence are determined for 48 distinct problems involving six models. Nonlinear regression is used to estimate run-time growth as a function of problem size. Both polynomial and exponential growth models are fit to the run-time data. For problems with random clutter, the polynomial model fits better and growth is comparable to that for tree search. For problems involving symmetric models and multiple model instances, where tree search is exponential, the polynomial growth model is superior to the exponential growth model for one search algorithm and comparable for another.