Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiscale Fourier Descriptor for Shape Classification
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Fast correspondence-based system for shape retrieval
Pattern Recognition Letters
A fast evaluation criterion for the recognition of occluded shapes
Robotics and Autonomous Systems
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Pattern Recognition
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ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
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ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Shape analysis for power signal cryptanalysis on secure components
Journal of Systems and Software
Shape classification via image-based multiscale description
Pattern Recognition
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
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We present a novel correspondence-based technique for efficient shape classification and retrieval. Shape boundaries are described by a set of (ad hoc) equally spaced points - avoiding the need to extract "landmark points". By formulating the correspondence problem in terms of a simple generative model, we are able to efficiently compute matches that incorporate scale, translation, rotation and reflection invariance. A hierarchical scheme with likelihood cut-off provides additional speed-up. In contrast to many shape descriptors, the concept of a mean (prototype) shape follows naturally in this setting. This enables model based classification, greatly reducing the cost of the testing phase. Equal spacing of points can be defined in terms of either perimeter distance or radial angle. It is shown that combining the two leads to improved classification/ retrieval performance.