Algorithm

Linguistics

Philosophy

Smoothing

Chromatography

Basis (linear algebra)

Set (abstract data type)

Data mining

Computer science

Pattern recognition (psychology)

Mathematics

Image (mathematics)

Feature (linguistics)

Artificial intelligence

Geometry

Point (geometry)

Chemistry

Programming language

Sample (material)

RANSAC

Landmark

Computer vision

Communications of the ACM

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

Random sample consensus