Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Special Issue on Tribute Workshop for Peter Johansen
Journal of Mathematical Imaging and Vision
Projected Generalized Procrustes Alignment
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
A Reflexion on Implementation Version for Active Appearance Model
International Journal of Computer Vision and Image Processing
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This paper describes a new method for resistant and robust alignment of sets of 2D shapes wrt. position, rotation, and iso-tropical scaling. Apart from robustness a major advantage of the method is that it is formulated as a linear programming (LP) problem, thus enabling the use of well known and thoroughly tested standard numerical software. The problem is formulated as the minimization of the norm of a linear vector function with a constraint of non-zero size. This is achieved by using the Manhattan distance between points in the plane. Unfortunately the Manhattan distance is dependent on the orientation of the coordinate system, i.e. it is not rotationally invariant. However, by simultaneously minimizing the Manhattan distances in a series of rotated coordinate systems we are able to approximate the circular equidistance curves of Euclidean distances with a regular polygonal equidistance curve to the precision needed. Using 3 coordinate systems rotated 30° we get a 12 sided regular polygon, with which we achieve deviations from Euclidean distances less than 2% over all directions. This new formulation allows for minimization in the L 1-norm using LP. We demonstrate that the use of the L 1-norm results in resistance towards object as well as landmark outliers. Examples that illustrate the properties of the robust norm are given on simulated as well as a biological data sets.