A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Context-free attentional operators: the generalized symmetry transform
International Journal of Computer Vision - Special issue on qualitative vision
Symmetry detection using gradient information
Pattern Recognition Letters
3D Symmetry Detection Using The Extended Gaussian Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern recognition in images by symmetries and coordinate transformations
Computer Vision and Image Understanding
Local operators to detect regions of interest
Pattern Recognition Letters - special issue on pattern recognition in practice V
Symmetry Detection by Generalized Complex (GC) Moments: A Close-Form Solution
IEEE Transactions on Pattern Analysis and Machine Intelligence
Symmetry as a Continuous Feature
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Radial Symmetry for Detecting Points of Interest
IEEE Transactions on Pattern Analysis and Machine Intelligence
Replicator Equations, Maximal Cliques, and Graph Isomorphism
Neural Computation
A planar-reflective symmetry transform for 3D shapes
ACM SIGGRAPH 2006 Papers
Dominant Sets and Pairwise Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Symmetry Preserving Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
SMI '07 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2007
IEEE Transactions on Computers
A continuous-based approach for partial clique enumeration
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
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Finding the most relevant symmetry planes for an object is a key step in many computer vision and object recognition tasks. In fact such information can be effectively used as a starting point for object segmentation, noise reduction, alignment and recognition. Some of these applications are strongly affected by the accuracy of symmetry planes estimation, thus the use of a technique that is both accurate and robust to noise is critical. In this paper we introduce a new weighted association graph which relates the main symmetry planes of 3D objects to large sets of tightly coupled vertices. This technique allows us to cast symmetry detection to a classical pairwise clustering problem, which we solve using the very effective Dominant Sets framework. The improvement of our approach over other well known techniques is shown with several tests over both synthetic data and sampled point clouds.