Object recognition in man, monkey, and machine
Object recognition in man, monkey, and machine
Shock Graphs and Shape Matching
International Journal of Computer Vision
Multidimensional binary search trees used for associative searching
Communications of the ACM
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Shape Analysis and Classification: Theory and Practice
Shape Analysis and Classification: Theory and Practice
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Investigating Hidden Markov Models' Capabilities in 2D Shape Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis of Planar Shapes Using Geodesic Paths on Shape Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Classifer Based on Generalized Probabilistic Descent Method with Hidden Markov Descriptor
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
R-Trees: Theory and Applications (Advanced Information and Knowledge Processing)
R-Trees: Theory and Applications (Advanced Information and Knowledge Processing)
Automatic detection of abnormal gait
Image and Vision Computing
2D-shape analysis using conformal mapping
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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This paper explores the use of tree-based data structures in shape analysis. We consider a structure which combines several properties of traditional tree models and obtain an efficiently compressed yet faithful representation of shapes. Constructed in a top-down fashion, the resulting trees are unbalanced but resolution adaptive. While the interior of a shape is represented by just a few nodes, the structure automatically accounts for more details at wiggly parts of a shape's boundary. Since its construction only involves simple operations, the structure provides an easy access to salient features such as concave cusps or maxima of curvature. Moreover, tree serialization leads to a representation of shapes by means of sequences of salient points. Experiments with a standard shape data-base reveal that correspondingly trained HMMs allow for robust classification. Finally, using spectral clustering, tree-based models also enable the extraction of larger, semantically meaningful, salient parts of shapes.