CTFDP: an affine invariant method for matching contours

Authors:
Hui-Xuan Tang;Hui Wei
Affiliations:
Lab of Algorithm for Cognitive Model, Department of Computer Science and Engineering, Fudan University, Shanghai, P.R. China;Lab of Algorithm for Cognitive Model, Department of Computer Science and Engineering, Fudan University, Shanghai, P.R. China
Venue:
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Year:
2005

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Abstract

In this paper a new method for matching contours called CTFDP is presented. It is invariant to affine transform and can provide robust and accurate estimation of point correspondence between closed curves. This has all been achieved by exploiting the dynamic programming techniques in a coarse-to-fine framework. By normalizing the shape into a standard point distribution, the new method can compare different shapes despite the shearing and scaling effect of affine transform. Using the coarse-to-fine dynamic programming technique, the shapes are aligned to each other by iteratively seeking correspondences and estimating relative transform so as to prune the start points in the dynamic programming stage in turn. Experiments on artificial and real images have validated the robustness and accuracy of the presented method.