Title : Random Sample Consensus: A Paradigm for Model Fitting with Apphcatlons to Image Analysis and Automated Cartography.
Authors : Martin A. Fischler and Robert C. Bolles SRI International(June, 1981)
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
and analysis conditions. Implementation details and
computational examples are also presented.
Key Words and Phrases: model fitting, scene
analysis, camera calibration, image matching, location
determination, automated cartography.
Link : Full text
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