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Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
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Communications of the ACM archive
Volume 24 ,  Issue 6  (June 1981) table of contents
Pages: 381 - 395  
Year of Publication: 1981
ISSN:0001-0782
Authors
Martin A. Fischler  SRI International, Menlo Park, CA
Robert C. Bolles  SRI International, Menlo Park, CA
Publisher
ACM  New York, NY, USA
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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


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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INDEX TERMS

Primary Classification:
  G. Mathematics of Computing

Additional Classification:
  I. Computing Methodologies
  I.6 SIMULATION AND MODELING


General Terms:
Design, Experimentation, Theory


Keywords:
automated cartography, camera calibration, image matching, location determination, model fitting, scene analysis

Collaborative Colleagues:
Martin A. Fischler: colleagues
Robert C. Bolles: colleagues

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