Random sample consensus (RANSAC) is an iterative method to estimate parameters of a mathematical model from a set of observed data which contains outliers. It is a non-deterministic algorithm in the sense that it produces a reasonable result only with a certain probability, with this probability increasing as more iterations are allowed. The algorithm was first published by Fischler and Bolles at SRI International in 1981.
A basic assumption is that the data consists of "inliers", i.e., data whose distribution can be explained by some set of model parameters, though may be subject to noise, and "outliers" which are data that do not fit the model. The outliers can come, e.g., from extreme values of the noise or from erroneous measurements or incorrect hypotheses about the interpretation of data. RANSAC also assumes that, given a (usually small) set of inliers, there exists a procedure which can estimate the parameters of a model that optimally explains or fits this data.
A simple example is fitting of a line in two dimensions to a set of observations. Assuming that this set contains both inliers, i.e., points which approximately can be fitted to a line, and outliers, points which cannot be fitted to this line, a simple least squares method for line fitting will generally produce a line with a bad fit to the inliers. The reason is that it is optimally fitted to all points, including the outliers. RANSAC, on the other hand, can produce a model which is only computed from the inliers, provided that the probability of choosing only inliers in the selection of data is sufficiently high. There is no guarantee for this situation,[clarification needed] however, and there are a number of algorithm parameters which must be carefully chosen to keep the level of probability reasonably high.
The input to the RANSAC algorithm is a set of observed data values, a way of fitting some kind of model to the observations, and some confidence parameters. RANSAC achieves its goal by repeating the following steps:
- Select a random subset of the original data. Call this subset the hypothetical inliers.
- A model is fitted to the set of hypothetical inliers.
- All other data are then tested against the fitted model. Those points that fit the estimated model well, according to some model-specific loss function, are considered as part of the consensus set.
- The estimated model is reasonably good if sufficiently many points have been classified as part of the consensus set.
- Afterwards, the model may be improved by reestimating it using all members of the consensus set.
This procedure is repeated a fixed number of times, each time producing either a model which is rejected because too few points are part of the consensus set, or a refined model together with a corresponding consensus set size. In the latter case, we keep the refined model if its consensus set is larger than the previously saved model.
The values of parameters t and d[clarification needed] have to be determined from specific requirements related to the application and the data set, possibly based on experimental evaluation. The parameter k (the number of iterations), however, can be determined from a theoretical result. Let p be the probability that the RANSAC algorithm in some iteration selects only inliers from the input data set when it chooses the n points from which the model parameters are estimated. When this happens, the resulting model is likely to be useful so p gives the probability that the algorithm produces a useful result. Let w be the probability of choosing an inlier each time a single point is selected, that is,
- w = number of inliers in data / number of points in data
A common case is that w is not well known beforehand, but some rough value can be given. Assuming that the n points needed for estimating a model are selected independently, is the probability that all n points are inliers and is the probability that at least one of the n points is an outlier, a case which implies that a bad model will be estimated from this point set. That probability to the power of k is the probability that the algorithm never selects a set of n points which all are inliers and this must be the same as . Consequently,
which, after taking the logarithm of both sides, leads to
This result assumes that the n data points are selected independently, that is, a point which has been selected once is replaced and can be selected again in the same iteration. This is often not a reasonable approach and the derived value for k should be taken as an upper limit in the case that the points are selected without replacement. For example, in the case of finding a line which fits the data set illustrated in the above figure, the RANSAC algorithm typically chooses two points in each iteration and computes
maybe_model as the line between the points and it is then critical that the two points are distinct.
To gain additional confidence, the standard deviation or multiples thereof can be added to k. The standard deviation of k is defined as
Advantages and disadvantages
|This section needs additional citations for verification. (September 2014)|
An advantage of RANSAC is its ability to do robust estimation of the model parameters, i.e., it can estimate the parameters with a high degree of accuracy even when a significant number of outliers are present in the data set. A disadvantage of RANSAC is that there is no upper bound on the time it takes to compute these parameters. When the number of iterations computed is limited the solution obtained may not be optimal, and it may not even be one that fits the data in a good way. In this way RANSAC offers a trade-off; by computing a greater number of iterations the probability of a reasonable model being produced is increased. Moreover, RANSAC is not always able to find the optimal set even for moderately contaminated sets and it usually performs badly when the number of inliers is less than 50%. Optimal RANSAC was proposed to handle both these problems and is capable of finding the optimal set for heavily contaminated sets, even for an inlier ratio under 5%. Another disadvantage of RANSAC is that it requires the setting of problem-specific thresholds.
RANSAC can only estimate one model for a particular data set. As for any one-model approach when two (or more) model instances exist, RANSAC may fail to find either one. The Hough transform is one alternative robust estimation technique that may be useful when more than one model instance is present. Another approach for multi model fitting is known as PEARL, which combines model sampling from data points as in RANSAC with iterative re-estimation of inliers and the multi-model fitting being formulated as an optimization problem with a global energy functional describing the quality of the overall solution.
- Martin A. Fischler and Robert C. Bolles (June 1981). "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography". Comm. of the ACM 24 (6): 381–395. doi:10.1145/358669.358692.
- David A. Forsyth and Jean Ponce (2003). Computer Vision, a modern approach. Prentice Hall. ISBN 0-13-085198-1.
- Richard Hartley and Andrew Zisserman (2003). Multiple View Geometry in Computer Vision (2nd ed.). Cambridge University Press.
- P.H.S. Torr and D.W. Murray (1997). "The Development and Comparison of Robust Methods for Estimating the Fundamental Matrix". International Journal of Computer Vision 24 (3): 271–300. doi:10.1023/A:1007927408552.
- Ondrej Chum (2005). "Two-View Geometry Estimation by Random Sample and Consensus". PhD Thesis.
- Sunglok Choi, Taemin Kim, and Wonpil Yu (2009). "Performance Evaluation of RANSAC Family". In Proceedings of the British Machine Vision Conference (BMVC).
- Anders Hast, Johan Nysjö, Andrea Marchetti (2013). "Optimal RANSAC – Towards a Repeatable Algorithm for Finding the Optimal Set". Journal of WSCG 21 (1): 21–30.
- Hossam Isack, Yuri Boykov (2012). "Energy-based Geometric Multi-Model Fitting". International Journal of Computer Vision 97 (2: 1): 23–147. doi:10.1007/s11263-011-0474-7.
- RANSAC Toolbox for MATLAB. A research (and didactic) oriented toolbox to explore the RANSAC algorithm in MATLAB. It is highly configurable and contains the routines to solve a few relevant estimation problems.
- ransac.m The RANSAC algorithm in MATLAB.
- optimalRansac.m The Optimal RANSAC algorithm in MATLAB.
- Implementation in C++ as a generic template.
- Implementation in C++ as a generic template with hyperplane and hypersphere examples.
- RANSAC for Dummies A simple tutorial with many examples that uses the RANSAC Toolbox for MATLAB.
- Source code for RANSAC in MATLAB
- ransac.py Python implementation for Scipy/Numpy.
- Scikit-learn and Scikit-image contains Python implementations.
- GML RANSAC Matlab Toolbox – a set of MATLAB scripts, implementing RANSAC algorithm family.
- RANSAC for estimation of geometric transforms - MATLAB examples and help on using RANSAC in Computer Vision applications