Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
On the Sensitivity of the Hough Transform for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing solid objects by alignment with an image
International Journal of Computer Vision
Local search algorithms for geometric object recognition: optimal correspondence and pose
Local search algorithms for geometric object recognition: optimal correspondence and pose
How Easy is Matching 2D Line Models Using Local Search?
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Development and Comparison of Robust Methodsfor Estimating the Fundamental Matrix
International Journal of Computer Vision
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Polynomial-Time Object Recognition in the Presence of Clutter, Occlusion, and Uncertainty
ECCV '92 Proceedings of the Second European Conference on Computer Vision
SoftPOSIT: Simultaneous Pose and Correspondence Determination
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Model-based image matching using location (pattern, recognition)
Model-based image matching using location (pattern, recognition)
Two-dimensional projective point matching
Two-dimensional projective point matching
Robust Computation and Parametrization of Multiple View Relations
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
SoftPOSIT: Simultaneous Pose and Correspondence Determination
International Journal of Computer Vision
Information Sciences: an International Journal
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Point matching is the task of finding correspondences between two sets of points such that the two sets of points are aligned with each other. Pure point matching uses only the location of the points to constrain the problem. This is a problem with broad practical applications, but it has only been well studied when the geometric transformation relating the two point sets is of a relatively low order. Here we present a heuristic local search algorithm that can find correspondences between point sets in two dimensions that are related by a projective transform. Point matching is a harder problem when spurious points appear in the sets to be matched. We present a heuristic algorithm which minimizes the effects of spurious points.