Model-based image matching using location
Model-based image matching using location
Computing the minimum Hausdorff distance for point sets under translation
SCG '90 Proceedings of the sixth annual symposium on Computational geometry
Recognizing solid objects by alignment with an image
International Journal of Computer Vision
Geometric aspects of visual object recognition
Geometric aspects of visual object recognition
Polynomial-time geometric matching for object recognition
Polynomial-time geometric matching for object recognition
Robust Affine Structure Matching for 3D Object Recognition
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Line feature-based recognition using Hausdorff distance
ISCV '95 Proceedings of the International Symposium on Computer Vision
ASOD: Arbitrary shape object detection
Engineering Applications of Artificial Intelligence
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The problem of matching two planar sets of points in the presence of geometric uncertainty has important applications in pattern recognition, image understanding, and robotics. The first set of points corresponds to the "template." The other set corresponds to the "image" that驴possibly驴contains one or more deformed versions of the "template" embedded in a cluttered image. Significant progress has been made on this problem and various polynomial-time algorithms have been proposed. In this article, we show how to sample the "image" in linear time, reducing the number of foreground points n by a factor of two-six (for commonly occurring images) without degrading the quality of the matching results. The direct consequence is a time-saving by a factor of 2p驴6p for an O(np) matching algorithm. Our result applies to a fairly large class of available matching algorithms.