The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Training Models of Shape from Sets of Examples
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Citations
Cites
Kass, M., Witkin, A., & Terzopoulos, D. (1988) · International Journal of Computer Vision · doi:10.1007/bf00133570 · Cited by 17,070
Morphometric Tools for Landmark Data: Geometry and Biology
Bookstein, F. L. (1992) · Cambridge University Press eBooks · doi:10.1017/cbo9780511573064 · Cited by 4,365
Generalized procrustes analysis
Gower, J. C. (1975) · Psychometrika · doi:10.1007/bf02291478 · Cited by 3,267
2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
(2001) · Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition CVPR 2001 CVPR-01 · doi:10.1109/cvpr.2001.990916 · Cited by 1,876
Morphometric Tools for Landmark Data.
Bookstein, F. (1998) · Biometrics · doi:10.2307/2534038 · Cited by 1,378
Dynamic 3D models with local and global deformations: deformable superquadrics
Terzopoulos, D., & Metaxas, D. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.85659 · Cited by 781
Feature extraction from faces using deformable templates
Yuille, A. L., Cohen, D. S., & Hallinan, P. W. (2003) · Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1989.37836 · Cited by 708
Active Shape Models — ‘Smart Snakes’
Cootes, T. F., & Taylor, C. J. (1992) · BMVC92 · doi:10.1007/978-1-4471-3201-1_28 · Cited by 661
Self-citationObject recognition by computer: the role of geometric constraints
(1992) · Choice Reviews Online · doi:10.5860/choice.29-2747 · Cited by 654
Model-based recognition in robot vision
Chin, R. T., & Dyer, C. R. (1986) · ACM Computing Surveys · doi:10.1145/6462.6464 · Cited by 567
Cited by
Jacob, G., Noble, J. A., Behrenbruch, C., Kelion, A. D., & Banning, A. P. (2002) · IEEE Transactions on Medical Imaging · doi:10.1109/42.996341 · Cited by 142
3D reconstruction of medical images from slices automatically landmarked with growing neural models
Angelopoulou, A., Psarrou, A., Garcia-Rodriguez, J., Orts-Escolano, S., Azorin-Lopez, J., & Revett, K. (2015) · Neurocomputing · doi:10.1016/j.neucom.2014.03.078 · Cited by 31
(2012) · The turkish online journal of educational technology · Cited by 27
Metadata
- Authors
- Cootes, T. F., Taylor, C. J., Cooper, D. H., & Graham, J.
- Venue
- BMVC92
- Publisher
- Springer London
- Published
- 1992
- Pages
- 9-18
- OpenAlex
- W2157876396
- Cited by
- 459
- References
- 18
- FWCI
- 29.99
- Type
- book chapter
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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- CoChat. (2026). Citation check: Training Models of Shape from Sets of Examples. https://app.cochat.ai/papers/10.1007/978-1-4471-3201-1_2
- The paper
- Cootes, T. F., Taylor, C. J., Cooper, D. H., & Graham, J. (1992). Training Models of Shape from Sets of Examples. BMVC92. https://doi.org/10.1007/978-1-4471-3201-1_2
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
