Constructing arrangements of lines and hyperplanes with applications
SIAM Journal on Computing
Object recognition and localization via pose clustering
Computer Vision, Graphics, and Image Processing
An Efficiently Computable Metric for Comparing Polygonal Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Visibility Partition for Affine Pattern Matching
DGCI '00 Proceedings of the 9th International Conference on Discrete Geometry for Computer Imagery
Journal of Algorithms
On finding a guard that sees most and a shop that sells most
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
On approximating the depth and related problems
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Partial and approximate symmetry detection for 3D geometry
ACM SIGGRAPH 2006 Papers
Journal of Mathematical Imaging and Vision
Probabilistic matching and resemblance evaluation of shapes in trademark images
Proceedings of the 6th ACM international conference on Image and video retrieval
Probabilistic matching of planar regions
Computational Geometry: Theory and Applications
Multiple polyline to polygon matching
ISAAC'05 Proceedings of the 16th international conference on Algorithms and Computation
Hi-index | 5.23 |
In order to determine the similarity between two planar shapes, which is an important problem in computer vision and pattern recognition, it is necessary to first match the two shapes as well as possible. As sets of allowed transformation to match shapes we consider translations, rigid motions, and similarities. We present a generic probabilistic algorithm based on random sampling for matching shapes which are modelled by sets of curves. The algorithm is applicable to the three considered classes of transformations. We analyze which similarity measure is optimized by the algorithm and give rigorous bounds on the number of samples necessary to get a prespecified approximation to the optimal match within a prespecified probability.