|Author||Fischler, Martin A. ♦ Bolles, Robert C.|
|Source||ACM Digital Library|
|Publisher||Association for Computing Machinery (ACM)|
|Subject Keyword||Camera calibration ♦ Location determination ♦ Image matching ♦ Scene analysis ♦ Automated cartography ♦ Model fitting|
|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|
|Description||Affiliation: SRI International, Menlo Park, CA (Fischler, Martin A.; Bolles, Robert C.)|
|Age Range||18 to 22 years ♦ above 22 year|
|Education Level||UG and PG|
|Learning Resource Type||Article|
|Publisher Place||New York|
|Journal||Communications of the ACM (CACM)|
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