Random sample consensus | RDL Research Database

Abstract

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

Keywords

RANSACSmoothingComputer scienceArtificial intelligenceLandmarkSample (material)Feature (linguistics)Computer visionSet (abstract data type)Basis (linear algebra)Image (mathematics)Point (geometry)Pattern recognition (psychology)AlgorithmData miningMathematics

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Publication Info

Year: 1981
Type: article
Volume: 24
Issue: 6
Pages: 381-395
Citations: 24523
Access: Closed

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APA Style

                            
                                    Martin A. Fischler, 
                                
                                    Robert C. Bolles
                                
                            (1981). 
                            Random sample consensus. 
                            Communications of the ACM
                            , 24
                            (6)
                            , 381-395.
                            https://doi.org/10.1145/358669.358692

Identifiers

DOI: 10.1145/358669.358692