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.
“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]
Section II describes RANSAC fitting an initial model from a small subset and expanding it with consistent points, and the Experimental Results report a case where RANSAC succeeded after a least-squares pruning heuristic failed. The claim overstates this as general superiority over least squares: the paper demonstrates the advantage in the presence of gross errors and says least squares can refine a model once a consensus set is found. (LLM, conf=0.85)
Experimental — accuracy not yet measuredGiven supervision via anatomical landmarks, a camera pose can be estimated by applying a PnP solver to corresponding landmarks.
“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]
IP algorithms based on keypoint feature detectors present advantages compared to algorithms based on edge and corner detectors, given their invariance to perspective, scale and illumination changes.
“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]
The Introduction discusses errors made by local feature detectors, and Section III discusses matching image points to landmarks. Neither passage compares keypoint-based algorithms with edge- or corner-based algorithms or addresses invariance to perspective, scale, and illumination changes; searches for those terms found no supporting passage in our copy. (LLM, conf=0.77)
Experimental — accuracy not yet measuredNeural 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.
“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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Depth and motion cues with phosphene patterns for prosthetic vision
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- 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 › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Keywords
- RANSAC, model fitting, gross errors, outlier removal, automated image analysis
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