The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Efficient matching of pictorial structures
Abstract
A pictorial structure is a collection of parts arranged in a deformable configuration. Each part is represented using a simple appearance model and the deformable configuration is represented by spring-like connections between pairs of parts. While pictorial structures were introduced a number of years ago, they have not been broadly applied to matching and recognition problems. This has been due in part to the computational difficulty of matching pictorial structures to images. In this paper we present an efficient algorithm for finding the best global match of a pictorial stucture to an image. With this improved algorithm, pictorial structures provide a practical and powerful framework for quantitative descriptions of objects and scenes, and are suitable for many generic image recognition problems. We illustrate the approach using simple models of a person and a car.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Representation and Matching of Pictorial Structures
Fischler, M. A., & Elschlager, R. A. (1973) · IEEE Transactions on Computers · doi:10.1109/t-c.1973.223602 · Cited by 1,322
Research works printed but not linked: 16
- 10.1109/34.232073 · DOI · Paper page
- 10.1109/CVPR.1998.698598 · DOI
- 10.1109/CVPR.1997.609317 · doi:10.1109/cvpr.1997.609317
- 10.1109/CVPR.1997.609453 · DOI
- 10.1109/34.134043 · DOI · Paper page
- 10.1007/BF01421486 · DOI · Paper page
- rucklidge, 1996, Efficient Visual Recognition Using the Hausdorff Distance, 1173, 10.1007/BFb0015091 · DOI
- 10.1007/BF00130487 · DOI · Paper page
- 10.1109/CVPR.1998.698673 · DOI · Paper page
- 10.1016/S0734-189X(86)80047-0 · DOI · Paper page
- burl, 1998, A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry, ECCV, 2, 628
- 10.1109/CVPR.1996.517078 · DOI
- 10.1007/BF00133555 · doi:10.1007/bf00133555
- 10.1109/CVPR.1991.139736 · doi:10.1109/cvpr.1991.139736
- 10.1109/34.57681 · DOI · Paper page
- hel-or, 1994, Constraint-Fusion for Localization and Interpretation of Constrained Objects, CVPR, 39
Cited by
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Duric, Z., Gray, W. D., Heishman, R., Li, F., Rosenfeld, A., Schoelles, M. J., Schunn, C., & Wechsler, H. (2002) · Proceedings of the IEEE · doi:10.1109/jproc.2002.801449 · Cited by 185
Bounding Part Scores for Rapid Detection with Deformable Part Models
Kokkinos, I. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33885-4_5 · Cited by 27
Metadata
- Authors
- Felzenszwalb, P. F., & Huttenlocher, D. P.
- Venue
- Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662)
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-07
- Volume
- 2
- Pages
- 66-73
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2097041931
- Cited by
- 310
- References
- 1
- FWCI
- 13.47
- Type
- conference paper
- 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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Efficient matching of pictorial structures. https://app.cochat.ai/papers/10.1109/cvpr.2000.854739
- The paper
- Felzenszwalb, P. F., & Huttenlocher, D. P. (2002). Efficient matching of pictorial structures. Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662). https://doi.org/10.1109/cvpr.2000.854739
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.
