Classification with Nonmetric Distances: Image Retrieval and Class Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using the Inner-Distance for Classification of Articulated Shapes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Theoretical and Computational Framework for Isometry Invariant Recognition of Point Cloud Data
Foundations of Computational Mathematics
Representation and matching of articulated shapes
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Analysis of Two-Dimensional Non-Rigid Shapes
International Journal of Computer Vision
Symmetry of Shapes Via Self-similarity
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Partial Similarity of Objects, or How to Compare a Centaur to a Horse
International Journal of Computer Vision
Paretian similarity for partial comparison of non-rigid objects
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Interactive on-surface signal deformation
ACM SIGGRAPH 2010 papers
Articulation-invariant representation of non-planar shapes
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
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We present a theoretical and computational framework for matching of two-dimensional articulated shapes. Assuming that articulations can be modeled as near-isometries, we show an axiomatic construction of an articulation-invariant distance between shapes, formulated as a generalized multidimensional scaling (GMDS) problem and solved efficiently. Some numerical results demonstrating the accuracy of our method are presented