Robust regression and outlier detection
Robust regression and outlier detection
Fast neighborhood search in planar point sets
Pattern Recognition Letters
Efficiently Locating Objects Using the Hausdorff Distance
International Journal of Computer Vision
Edge-Based Robust Image Registration for Incomplete and Partly Erroneous Data
CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
Bag-of-GraphPaths descriptors for symbol recognition and spotting in line drawings
GREC'11 Proceedings of the 9th international conference on Graphics Recognition: new trends and challenges
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The problem of defect detection in 2D and 3D shapes is analyzed. A shape is represented by a set of its contour, or surface, points. Mathematically, the problem is formulated as a specific matching of two sets of points, a reference one and a measured one. Modified Hausdorff distance between these two point sets is used to induce the matching. Based on a distance transform, a 2D algorithm is proposed that implements the matching in a computationally efficient way. The method is applied to visual inspection and dimensional measurement of ferrite cores. Alternative approaches to the problem are also discussed.1