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Object recognition by computer: the role of geometric constraints
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Shock Graphs and Shape Matching
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A SIFT Descriptor with Global Context
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Shape context and chamfer matching in cluttered scenes
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Recognizing objects in adversarial clutter: breaking a visual captcha
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Learning a discriminative classifier using shape context distances
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
On bending invariant signatures for surfaces
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Distance sets for shape filters and shape recognition
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Shape representation and recognition through morphological curvature scale spaces
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Applied Multidimensional Scaling
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Robust symbolic representation for shape recognition and retrieval
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ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Shape of Elastic Strings in Euclidean Space
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PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
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Shape classification using complex network and Multi-scale Fractal Dimension
Pattern Recognition Letters
Deformable 2D Shape Matching Based on Shape Contexts and Dynamic Programming
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Toward a vision based hand gesture interface for robotic grasping
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
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SGP '09 Proceedings of the Symposium on Geometry Processing
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International Journal of Computer Vision
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Image and Vision Computing
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Pattern Recognition Letters
Projective analysis for 3D shape segmentation
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Information Sciences: an International Journal
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Multimedia Tools and Applications
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Part structure and articulation are of fundamental importance in computer and human vision. We propose using the inner-distance to build shape descriptors that are robust to articulation and capture part structure. The inner-distance is defined as the length of the shortest path between landmark points within the shape silhouette. We show that it is articulation insensitive and more effective at capturing part structures than the Euclidean distance. This suggests that the inner-distance can be used as a replacement for the Euclidean distance to build more accurate descriptors for complex shapes, especially for those with articulated parts. In addition, texture information along the shortest path can be used to further improve shape classification. With this idea, we propose three approaches to using the inner-distance. The first method combines the inner-distance and multidimensional scaling (MDS) to build articulation invariant signatures for articulated shapes. The second method uses the inner-distance to build a new shape descriptor based on shape contexts. The third one extends the second one by considering the texture information along shortest paths. The proposed approaches have been tested on a variety of shape databases, including an articulated shape data set, MPEG7 CE-Shape-1, Kimia silhouettes, the ETH-80 data set, two leaf data sets, and a human motion silhouette data set. In all the experiments, our methods demonstrate effective performance compared with other algorithms.