Surface shape and curvature scales
Image and Vision Computing
COSMOS-A Representation Scheme for 3D Free-Form Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Point Signatures: A New Representation for 3D Object Recognition
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
Optimizing 3D triangulations using discrete curvature analysis
Mathematical Methods for Curves and Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Polyhedral Metrics in Surface Reconstruction
Proceedings of the 6th IMA Conference on the Mathematics of Surfaces
Recognizing Objects by Matching Oriented Points
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Free-Form Surface Registration Using Surface Signatures
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A Flexible Similarity Measure for 3D Shapes Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Directional histogram model for three-dimensional shape similarity
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A New Segmentation Approach for Old Fractured Pieces
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Extended cone-curvature based salient points detection and 3D model retrieval
Multimedia Tools and Applications
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This work faces the problem of 3D shape clustering when the whole surface information is available. The key of our method is to use a flexible feature, called Cone-Curvature, which provides local and extended information around every node of the mesh that represents the object. Thus as we increase the region around a node a new order of CC can be calculated. This feature, which was originally defined on spherical representation, has been adapted to work with standard triangular meshes and it is used for defining a similarity measure between shapes. Through a PCA technique, the dimensionality of the shape representation is drastically reduced and the hierarchical grouping can be efficiently carried out. This method has been tested under real conditions for a wide set of free shapes yielding promising results. We present a discussion of the clustering comparing human and computer results.