Model-based image matching using location
Model-based image matching using location
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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
Geometric aspects of visual object recognition
Geometric aspects of visual object recognition
Randomized Hough transform (RHT): basic mechanisms, algorithms, and computational complexities
CVGIP: Image Understanding
Statistical Approaches to Feature-Based Object Recognition
International Journal of Computer Vision
How Easy is Matching 2D Line Models Using Local Search?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficiently Locating Objects Using the Hausdorff Distance
International Journal of Computer Vision
Uncertainty Propagation in Model-Based Recognition
International Journal of Computer Vision
Solution of the simultaneous pose and correspondence problem using Gaussian error model
Computer Vision and Image Understanding
Line-Based Recognition Using A Multidimensional Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reliable and Efficient Pattern Matching Using an Affine Invariant Metric
International Journal of Computer Vision
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Search Strategies for Geometric Branch and Bound Algorithms
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Two Geometric Algorithms for Layout Analysis
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
A Probabilistic Formulation for Hausdorff Matching
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Document Signature Using Intrinsic Features for Counterfeit Detection
IWCF '08 Proceedings of the 2nd international workshop on Computational Forensics
Robust Optimal Pose Estimation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Global Optimization through Rotation Space Search
International Journal of Computer Vision
Automatic Line Orientation Measurement for Questioned Document Examination
IWCF '09 Proceedings of the 3rd International Workshop on Computational Forensics
A branch & bound algorithm for medical image registration
IWCIA'08 Proceedings of the 12th international conference on Combinatorial image analysis
Recognition driven page orientation detection
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Stochastic global optimization for robust point set registration
Computer Vision and Image Understanding
Robust fitting for multiple view geometry
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Global optimization in discretized parameter space for predefined object segmentation
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
On the use of geometric matching for both: isolated symbol recognition and symbol spotting
GREC'11 Proceedings of the 9th international conference on Graphics Recognition: new trends and challenges
Building a symbol library from technical drawings by identifying repeating patterns
GREC'11 Proceedings of the 9th international conference on Graphics Recognition: new trends and challenges
Fast rotation search for real-time interactive point cloud registration
Proceedings of the 18th meeting of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games
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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 objcct 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.