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

Authors:

Martin A. Fischler,

Robert C. BollesAuthors Info & Claims

Communications of the ACM, Volume 24, Issue 6

Pages 381 - 395

https://doi.org/10.1145/358669.358692

Published: 01 June 1981 Publication History

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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

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Index Terms

Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation
2. Mathematics of computing
  1. Probability and statistics

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Published In

Communications of the ACM Volume 24, Issue 6

June 1981

59 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/358669

Editor:
Robert L. Ashenhurst
Univ. of Chicago, Chicago, IL

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 01 June 1981

Published in CACM Volume 24, Issue 6

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Cited By

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A Vision Based Position System for Robot Picking

Image Matching based on Co-Motion Statistics

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