Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Getting around a lower bound for the minimum Hausdorff distance
Computational Geometry: Theory and Applications
A modified Hausdorff distance between fuzzy sets
Information Sciences: an International Journal
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rotation-invariant pattern matching using wavelet decomposition
Pattern Recognition Letters
FPGA-Based Template Matching Using Distance Transforms
FCCM '02 Proceedings of the 10th Annual IEEE Symposium on Field-Programmable Custom Computing Machines
Translation, scaling and rotation invariant spot matching using delaunay triangulation
ACS'08 Proceedings of the 8th conference on Applied computer scince
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Feature correspondence is an important step in image registration. Chamfer matching is a process of establishing the feature correspondence of an object (subimage) in an image where both the subimage and the image are binary. Chamfer matching establishes correspondence based on low level features. It is the process of locating the template within the image by shifting the template within the image and at each shift position determining the sum of distances of closest object points in the template and the image. The smaller the sum, the closer object points in the template and the image. Correspondence is achieved by calculating the minimum distance out of all the translations which is computationally expensive. The problem is further aggravated if reference image is of much greater dimensions as compared to template image. Since the reference image consists of objects and background, calculating the minimum distance for all the pixel locations becomes time consuming. In this paper a method to decrease the computation time of existing technique is presented, to make existing schemes suitable for real time registration.