NDLI: Model Fitting with Sufficient Random Sample Coverage

Content Provider	Springer Nature Link
Author	Scherer Negenborn, rbert Schaefer, Rolf
Copyright Year	2010
Abstract	It has been observed previously that the number of iterations required to derive good model parameter values used by RANSAC-like model estimators is too optimistic. We present the derivation of an analytical formula that allows the calculation of the sufficient limit of iterations needed to obtain good parameter values with the prescribed probability for any number of model parameters. It explains the values that had been found experimentally for certain numbers of model parameters by others very well. Furthermore, the improvement that our approach of SUfficient Random SAmple Coverage (SURSAC) offers, in comparison to the original RANSAC algorithm as well as to its adaptive modification by Hartley and Zisserman, is demonstrated with synthetic data for the case of a non-linear model function over a wide range of outlier fractions and different ratios of inlier and outlier densities.
Starting Page	120
Ending Page	128
Page Count	9
File Format	PDF
ISSN	09205691
Journal	International Journal of Computer Vision
Volume Number	89
Issue Number	1
e-ISSN	15731405
Language	English
Publisher	Springer US
Publisher Date	2010-03-19
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Model fitting Probabilistic algorithms RANSAC Robust regression Pattern Recognition Image Processing and Computer Vision Artificial Intelligence (incl. Robotics) Computer Imaging, Vision, Pattern Recognition and Graphics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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