Random sample consensus

• Martin A. Fischler

  SRI International, Menlo Park, CA

• Robert C. Bolles

  SRI International, Menlo Park, CA

書誌事項

タイトル別名

• a paradigm for model fitting with applications to image analysis and automated cartography

抄録

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

収録刊行物

• Communications of the ACM

  Communications of the ACM 24 (6), 381-395, 1981-06

  Association for Computing Machinery (ACM)

キーワード

• General Computer Science

詳細情報

• CRID: 1360292619284965760
• NII論文ID: 30022110434
• DOI: 10.1145/358669.358692
• ISSN: 15577317
• ISSN: 00010782
• Web Site: https://dl.acm.org/doi/pdf/10.1145/358669.358692
• データソース種別:
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  • CiNii Articles