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Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
Open accessAbstract
A new paradigm, Random Sample Consensus (RANSAC), for fitting a model to experimental data is introduced. RANSAC is capable of interpreting/smoothing data containing a significant percentage of gross errors, and is thus ideally suited for applications in automated image analysis where interpretation is based on the data provided by error-prone feature detectors. A major portion of this paper describes the application of RANSAC to the Location Determination Problem (LDP): Given an image depicting a set of landmarks with known locations, determine that point in space from which the image was obtained. In response to a RANSAC requirement, new results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form. These results provide the basis for an automatic system that can solve the LDP under difficult viewing
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“RANSAC works better than LS method because RANSAC only needs to select a set of inliers to get a good model, instead of selecting all the data points.”
From Nyquist Zone Index and Chirp Rate Estimation of LFM Signal Intercepted by Nyquist Folding Receiver Based on Random Sample Consensus and Fractional Fourier Transform (2019) · cites this paper as [25]
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“Given supervision via anatomical landmarks, a camera pose can be estimated by applying a PnP solver [19] to corresponding landmarks extracted from 2D intraoperative and 3D preoperative imaging.”
From Intraoperative 2D/3D Image Registration via Differentiable X-Ray Rendering (2024) · cites this paper as [19]
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“As a general remark, IP algorithms based on keypoint features detectors present some advantages compared to algorithms based on edge and corner detectors, given their invariance to perspective, scale and illumination changes [34,41].”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [34]
Experimental — accuracy not yet measuredIn the citing paper
“Neural networks have been successfully applied as a robust solution to many computer vision problems due to their strength in generalizing from one dataset to another, but have rarely been used in space applications due to a perceived lack of confidence in their measurements [4].”
From Lunar Terrain Relative Navigation Using a Convolutional Neural Network for Visual Crater Detection (2020) · cites this paper as [4]
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Cites
(1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895
Elementary Numerical Analysis.
G., W., & ATKINSON, K. E. (1994) · Mathematics of Computation · doi:10.2307/2153423 · Cited by 1,257
Mathematical elements for computer graphics
Rogers, D. F., & Adams, J. A. (1976) · CERN Document Server (European Organization for Nuclear Research) · Cited by 1,060
Pattern Classification and Scene Analysis.
Grenander, U., Duda, R. O., & Hart, P. E. (1974) · Journal of the American Statistical Association · doi:10.2307/2286028 · Cited by 1,035
Least-squares estimation: from Gauss to Kalman
Sorenson, H. W. (1970) · IEEE Spectrum · doi:10.1109/mspec.1970.5213471 · Cited by 751
Dresel, L. A. G. (1966) · The Computer Journal · doi:10.1093/comjnl/9.2.199-a · Cited by 325
Mälzer, H. (1975) · Photogrammetria · doi:10.1016/0031-8663(75)90059-9 · Cited by 152
Introduction to Projective Geometry
Wylie, C. R. (1970) · CERN Document Server (European Organization for Nuclear Research) · Cited by 34
Research works printed but not linked: 6
- Bolles , R.C. , Quam , L.H. , Fischler , M.A. , and Wolf , H.C . The SRI road expert: Image to database correspondence . In Proc. Image Understanding Workshop , Pittsburgh, Pennsylvania , Nov. , 1978 , Bolles, R.C., Quam, L.H., Fischler, M.A., and Wolf, H.C. The SRI road expert: Image to database correspondence. In Proc. Image Understanding Workshop, Pittsburgh, Pennsylvania, Nov., 1978,
- Chrystal , G. Textbook of Algebra ( Vol 1 ) . Chelsea, New York, New York 1964 , p. 415. Chrystal, G. Textbook of Algebra (Vol 1). Chelsea, New York, New York 1964, p. 415.
- Church , E. Revised geometry of the aerial photograph. Bull. Aerial Photogrammetry. 15 , 1945 , Syracuse University . Church, E. Revised geometry of the aerial photograph. Bull. Aerial Photogrammetry. 15, 1945, Syracuse University.
- Dehn , E. Algebraic Equations . Dover , New York , 1960 . Dehn, E. Algebraic Equations. Dover, New York, 1960.
- Gennery , D.B. Least-squares stereo-camera calibration. Stanford Artificial Intelligence Project Internal Memo, Stanford , CA 1975. Gennery, D.B. Least-squares stereo-camera calibration. Stanford Artificial Intelligence Project Internal Memo, Stanford, CA 1975.
- Keller , M. and Tewinkel , G.C . Space resection in photogrammetry. ESSA Tech. Rept C&GS 32 , 1966 , U.S. Coast and Geodetic Survey . Keller, M. and Tewinkel, G.C. Space resection in photogrammetry. ESSA Tech. Rept C&GS 32, 1966, U.S. Coast and Geodetic Survey.
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Maitin-Shepard, J., Cusumano-Towner, M., Lei, J., & Abbeel, P. (2010) · 2010 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.2010.5509439 · Cited by 418
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Gorrochategui, E., Jaumot, J., Lacorte, S., & Tauler, R. (2016) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2016.07.004 · Cited by 352
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Metadata
- Authors
- Fischler, M. A., & Bolles, R. C.
- Affiliations
- SRI International (US)
- Venue
- Communications of the ACM
- Published
- 1981-06
- Volume
- 24
- Issue
- 6
- Pages
- 381-395
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2085261163
- Cited by
- 25,893
- References
- 8
- Citations by year
- 2026: 1256 · 2025: 1629 · 2024: 1627 · 2023: 1490 · 2022: 1328 · 2021: 1530 · 2020: 1340 · 2019: 1349 · 2018: 1326 · 2017: 1294 · 2016: 1213 · 2015: 1370 · 2014: 1278 · 2013: 1235 · 2012: 1084
- FWCI
- 12.00
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Communications of the ACM (publishedVersion)
- Funders
- Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Object Detection Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Keywords
- RANSAC, model fitting, gross errors, outlier removal, automated image analysis
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