Distance transformations in digital images
Computer Vision, Graphics, and Image Processing
Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of image registration techniques
ACM Computing Surveys (CSUR)
Efficiently Locating Objects Using the Hausdorff Distance
International Journal of Computer Vision
Multidimensional alignment using the Euclidean distance transform
Graphical Models and Image Processing
Matching for Shape Defect Detection
CAIP '99 Proceedings of the 8th International Conference on Computer Analysis of Images and Patterns
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In image registration it is vital to perform matching of those points in a pair of images which actually match each other, and to postpone those which do not match. It is not always known in advance, however, which points have their counterparts, and where are they located. To overcome this, we propose to use the Hausdorff distance function modified by using a voting scheme as a fitting quality function. This known function performs very well in guiding the matching process and supports stable matches even for low quality data. It also makes it possible to speed up the algorithms in various ways. An application to accuracy assessment of oncological radiotherapy is presented. Low contrast of images used to perform this task makes this application a challenging test.