The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Contour tracking by stochastic propagation of conditional density
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Citations
Cites
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949
Kass, M., Witkin, A., & Terzopoulos, D. (1988) · International Journal of Computer Vision · doi:10.1007/bf00133570 · Cited by 17,070
Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (1994) · Cited by 15,411
Adaptive filtering, prediction and control
Ioannou, P. (1985) · Automatica · doi:10.1016/0005-1098(85)90014-7 · Cited by 4,480
Adaptive filtering prediction and control
Goodwin, G. C., & Sin, K. S. (1984) · Cited by 3,400
Introduction to Stochastic Control Theory
(1970) · Mathematics in Science and Engineering · doi:10.1016/s0076-5392(09)x6015-7 · Cited by 3,220
Bar-Shalom, Y., Fortmann, T. E., & Cable, P. G. (1990) · The Journal of the Acoustical Society of America · doi:10.1121/1.398863 · Cited by 3,070
An Introduction to Splines for Use in Computer Graphics and Geometric Modeling
Bartels, R. H., Beatty, J. C., & Barsky, B. A. (1987) · Cumulative Index of Computer Aided Architectural Design · Cited by 1,144
Recognition by linear combinations of models
Ullman, S., & Basri, R. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.99234 · Cited by 809
Cited by
CONDENSATION—Conditional Density Propagation for Visual Tracking
Isard, M., & Blake, A. (1998) · International Journal of Computer Vision · doi:10.1023/a:1008078328650 · Cited by 4,886
Self-citationIncremental Learning for Robust Visual Tracking
Ross, D. A., Lim, J., Lin, R.-S., & Yang, M.-H. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0075-7 · Cited by 3,061
An introduction to MCMC for machine learning.
Andrieu, C., de Freitas, N., Doucet, A., & Jordan, M. I. (2003) · Machine Learning · doi:10.1023/a:1020281327116 · Cited by 2,451
Robust object tracking with online multiple instance learning
Babenko, B., Yang, M.-H., & Belongie, S. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.226 · Cited by 2,007
Robust Monte Carlo localization for mobile robots
Thrun, S., Fox, D., Burgard, W., & Dellaert, F. (2001) · Artificial Intelligence · doi:10.1016/s0004-3702(01)00069-8 · Cited by 1,809
Metadata
- Authors
- Isard, M., & Blake, A.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 1996
- Volume
- 1064
- Pages
- 343-356
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1481420047
- Cited by
- 869
- References
- 32
- FWCI
- 17.21
- Type
- conference paper
- Language
- en
- Open access
- closed
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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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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: Contour tracking by stochastic propagation of conditional density. https://app.cochat.ai/papers/10.1007/bfb0015549
- The paper
- Isard, M., & Blake, A. (1996). Contour tracking by stochastic propagation of conditional density. Lecture Notes in Computer Science. https://doi.org/10.1007/bfb0015549
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.
