NDLI: Recursive RANSAC : Multiple Signal Estimation with Outliers

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Recursive RANSAC : Multiple Signal Estimation with Outliers

Content Provider	Semantic Scholar
Author	Niedfeldt, Peter C. Beard, Randal W.
Copyright Year	2013
Abstract	The random sample consensus (RANSAC) algorithm is frequently used in computer vision to estimate the parameters of a signal in the presence of noisy and even spurious observations called gross errors. Instead of just one signal, we desire to estimate the parameters of multiple signals, where at each time step a set of observations of generated from the underlying signals and gross errors are received. In this paper, we develop the recursive RANSAC (RRANSAC) algorithm to solve the inherent data association problem and recursively estimate the parameters of multiple signals without prior knowledge of the number of true signals. We compare the performance of RRANSAC with several existing algorithms, and also demonstrate the capabilities of RRANSAC in an aerial geolocation problem.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://www.et.byu.edu/~beard/papers/preprints/NeidfeldtBeard13.pdf
Alternate Webpage(s)	http://www.nt.ntnu.no/users/skoge/prost/proceedings/nolcos-2013/papers/0213.pdf
Alternate Webpage(s)	http://folk.ntnu.no/skoge/prost/proceedings/nolcos-2013/papers/0213.pdf
Language	English
Access Restriction	Open
Subject Keyword	Aerial photography Airborne Ranger Algorithm Computational complexity theory Computer vision Convergence (action) Correspondence problem Geolocation Hough transform Kalman filter Physical object Programming paradigm Random sample consensus Randomness Recursion (computer science) Recursive Partitioning Assessment Recursive least squares filter Restless Legs Syndrome Simulation Stationary process Synthetic intelligence
Content Type	Text
Resource Type	Article

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