The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Fitting parameterized three-dimensional models to images
Abstract
Model-based recognition and motion tracking depend upon the ability to solve for projection and model parameters that will best fit a 3-D model to matching 2-D image features. The author extends current methods of parameter solving to handle objects with arbitrary curved surfaces and with any number of internal parameters representing articulation, variable dimensions, or surface deformations. Numerical stabilization methods are developed that take account of inherent inaccuracies in the image measurements and allow useful solutions to be determined even when there are fewer matches than unknown parameters. The Levenberg-Marquardt method is used to always ensure convergence of the solution. These techniques allow model-based vision to be used for a much wider class of problems than was possible with previous methods. Their application is demonstrated for tracking the motion of curved, parameterized objects.>
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
An Algorithm for Least-Squares Estimation of Nonlinear Parameters
Marquardt, D. W. (1963) · Journal of the Society for Industrial and Applied Mathematics · doi:10.1137/0111030 · Cited by 30,728
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893
A method for the solution of certain non-linear problems in least squares
Levenberg, K. (1944) · Quarterly of Applied Mathematics · doi:10.1090/qam/10666 · Cited by 12,359
Solutions of Ill-Posed Problems.
Bell, J. B., Tikhonov, A. N., & Arsenin, V. Y. (1978) · Mathematics of Computation · doi:10.2307/2006360 · Cited by 8,101
Solving Least Squares Problems
Lawson, C. L., & Hanson, R. J. (1995) · doi:10.1137/1.9781611971217 · Cited by 7,103
Marr, D., & Hildreth, E. (1980) · Proceedings of the Royal Society of London. Series B. Biological Sciences · doi:10.1098/rspb.1980.0020 · Cited by 6,510
MACHINE PERCEPTION OF THREE-DIMENSIONAL SOLIDS,
Roberts, L. G. (1963) · Defense Technical Information Center (DTIC) · Cited by 1,551
Computational vision and regularization theory
Poggio, T., Torre, V., & Koch, C. (1985) · Nature · doi:10.1038/317314a0 · Cited by 1,412
Three-dimensional object recognition from single two-dimensional images
Lowe, D. G. (1987) · Artificial Intelligence · doi:10.1016/0004-3702(87)90070-1 · Cited by 1,283
Self-citationPerceptual organization and visual recognition
(1985) · Computer Vision, Graphics, and Image Processing · doi:10.1016/0734-189x(85)90041-6 · Cited by 1,022
Cited by
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Self-citationObject recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
Self-citationActive Shape Models-Their Training and Application
Cootes, T. F., Taylor, C. J., Cooper, D. H., & Graham, J. (1995) · Computer Vision and Image Understanding · doi:10.1006/cviu.1995.1004 · Cited by 7,190
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
EPnP: An Accurate O(n) Solution to the PnP Problem
Lepetit, V., Moreno-Noguer, F., & Fua, P. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-008-0152-6 · Cited by 3,298
Metadata
- Authors
- Lowe, D. G.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1991-05
- Volume
- 13
- Issue
- 5
- Pages
- 441-450
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2096600681
- Cited by
- 837
- References
- 24
- FWCI
- 22.43
- Type
- journal article
- 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: Fitting parameterized three-dimensional models to images. https://app.cochat.ai/papers/10.1109/34.134043
- The paper
- Lowe, D. G. (1991). Fitting parameterized three-dimensional models to images. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.134043
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.
