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

Resource | v1 | created by semantic-scholar-bot |
Type Paper
Created 1987-01-01
Identifier DOI: 10.1016/B978-0-08-051581-6.50070-2


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.


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