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Shape descriptors for non-rigid shapes with a single closed contour
Abstract
The Core Experiment CE-Shape-1 for shape descriptors performed for the MPEG-7 standard gave a unique opportunity to compare various shape descriptors for non-rigid shapes with a single closed contour. There are two main differences with respect to other comparison results reported in the literature: (1) For each shape descriptor the experiments were carried out by an institute that is in favor of this descriptor. This implies that the parameters for each system were optimally determined and the implementations were thoroughly rested. (2) It was possible to compare the performance of shape descriptors based on totally different mathematical approaches. A more theoretical comparison of these descriptors seems to be extremely hard. In this paper we report on the MPEG-7 Core Experiment CE-Shape.
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Cited by
Shape matching and object recognition using shape contexts
Belongie, S., Malik, J., & Puzicha, J. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.993558 · Cited by 6,306
Classification of time series by shapelet transformation
Hills, J., Lines, J., Baranauskas, E., Mapp, J., & Bagnall, A. (2014) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-013-0322-1 · Cited by 538
Recognition of Shapes by Editing Shock Graphs
Sebastian, T., Klein, P. N., Kimia, B. B., & Ri, P. (2001) · doi:10.1109/iccv.2001.10050 · Cited by 285
Metadata
- Authors
- Latecki, L. J., Lakamper, R., & Eckhardt, T.
- Venue
- Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662)
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-07
- Volume
- 1
- Pages
- 424-429
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2106404777
- Cited by
- 833
- References
- 0
- FWCI
- 8.24
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Latecki, L. J., Lakamper, R., & Eckhardt, T. (2002). Shape descriptors for non-rigid shapes with a single closed contour. Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662). https://doi.org/10.1109/cvpr.2000.855850
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