Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

@article{Fischler1981RandomSC,
  title={Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography},
  author={Martin A. Fischler and Robert C. Bolles},
  journal={Commun. ACM},
  year={1981},
  volume={24},
  pages={381-395}
}
A new paradigm, Random Sample Consensus (RANSAC), for fitting a model to experimental data is introduced. [] Key Result These results provide the basis for an automatic system that can solve the LDP under difficult viewing

Figures and Tables from this paper

A probabilistic analysis of a common RANSAC heuristic

A probabilistic analysis of this common heuristic and the possibility of finding an optimal size for the randomly sampled data points per iteration of RANSAC is explored and the lower bound for the number of iterations of RansAC required to recover the model parameters is improved.

Randomized RANSAC with T(d, d) test

A new randomized (hypothesis evaluation) version of the RANSAC algorithm, R-RANSAC, is introduced and a mathematically tractable class of statistical preverification tests for test samples is introduced that derives an approximate relation for the optimal setting of its single parameter.

Randomized RANSAC with Td, d test

Classification Performance of RanSaC Algorithms with Automatic Threshold Estimation

This paper compares the RanSaC methods that simultaneously fit a model and estimate an appropriate threshold, and measures their classification performance on those semisynthetic feature correspondence pairs for homography, fundamental, and essential matrices.

A Simple Sample Consensus Algorithm to Find Multiple Models

The MuSAC algorithm is used to find models from 2D range images on cluttered environments with promising results and a novel distance for laser range sensors is introduced.

Model-free Consensus Maximization for Non-Rigid Shapes

This paper forms the model-free consensus maximization as an Integer Program in a graph using ‘rules’ on measurements and provides a method to solve it optimally using the Branch and Bound (BnB) paradigm.

Overview of the RANSAC Algorithm

The RANdom SAmple Consensus (RANSAC) algorithm proposed by Fischler and Bolles [1] is a general parameter estimation approach designed to cope with a large proportion of outliers in the input data.

A RANSAC-Based Approach to Model Fitting and Its Application to Finding Cylinders in Range Data

The technique is specifically designed to filter out gross errors before applying a smoothing procedure to compute a precise model in order to solve the problem of locating cylinders in range data.

DIRSAC: A directed sampling and consensus approach to quasi-degenerate data fitting

  • C. BakerW. Hoff
  • Computer Science
    2013 IEEE Workshop on Applications of Computer Vision (WACV)
  • 2013
This paper proposes a new data fitting method which, similar to RANSAC, fits data to a model using sample and consensus, and selects points based on a Mutual Information criterion, which allows to avoid redundant points that result in degenerate sample sets.

Expert Sample Consensus Applied to Camera Re-Localization

This work introduces Expert Sample Consensus (ESAC), which integrates DSAC in a MoE, and demonstrates experimentally that ESAC handles two real-world problems better than competing methods, i.e. scalability and ambiguity.
...

References

SHOWING 1-10 OF 10 REFERENCES

Least-squares estimation: from Gauss to Kalman

This discussion is directed to least-squares estimation theory, from its inception by Gauss1 to its modern form, as developed by Kalman.2 To aid in furnishing the desired perspective, the

Pattern classification and scene analysis

The topics treated include Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.

Elementary Numerical Analysis

Elementary Numerical Analysis

The SRI road expert: Image to database correspondence

  • Proc. Image Understanding Workshop
  • 1978

Space resection in photogrammetry

  • Space resection in photogrammetry
  • 1966

Textbook of Algebra (Vol

  • 1964

Revised geometry of the aerial photograph

  • Bull. Aerial Photogrammetry
  • 1945

Least-squares stereo-camera calibration

  • Stanford Artificial Intelligence Project Internal Memo
  • 1975