Approximation of the Euclidean Distance by Chamfer Distances

Approximation of the Euclidean Distance by Chamfer Distances

Chamfer distances play an important role in the theory of distance transforms. Though the determination of the exact Euclidean distance transform is also a well investigated area, the classical chamfering method based upon ”small” neighborhoods still outperforms it e.g. in terms of computation time. In this paper we determine the best possible maximum relative error of chamfer distances under v...

Approximations of the Euclidean distance by chamfer distances

Chamfer distances play an important role in the theory of distance transforms. Though the determination of the exact Euclidean distance transform is also a well investigated area, the classical chamfering method based upon ”small” neighborhoods still outperforms it e.g. in terms of computation time. In this paper we determine the best possible maximum relative error of chamfer distances under v...

Boosting Chamfer Matching by Learning Chamfer Distance Normalization

We propose a novel technique that significantly improves the performance of oriented chamfer matching on images with cluttered background. Different to other matching methods, which only measures how well a template fits to an edge map, we evaluate the score of the template in comparison to auxiliary contours, which we call normalizers. We utilize AdaBoost to learn a Normalized Oriented Chamfer...

Optimum Design of Chamfer Distance

The distance transform has found many applications in image analysis. The Euclidean distance transform is computationally very intensive, and eecient discrete algorithms based on chamfer metrics are used to obtain its approximations. The chamfer metrics are selected to minimize an approximation error. In this paper, a new approach is developed to nd optimal chamfer local distances. This new app...

Computing 3D Medial Axis for Chamfer Distances