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Content Provider | IEEE Xplore Digital Library |
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Author | Jongmoo Choi Medioni, G. |
Copyright Year | 2009 |
Description | Author affiliation: Inst. of Robot. & Intell. Syst., Univ. of Southern California, Los Angeles, CA, USA (Jongmoo Choi; Medioni, G.) |
Abstract | We address the problem of parameter estimation in presence of both uncertainty and outlier noise. This is a common occurrence in computer vision: feature localization is performed with an inherent uncertainty which can be described as Gaussian, with unknown variance; feature matching in multiple images produces incorrect data points. RANSAC is the preferred method to reject outliers if the variance of the uncertainty noise is known, but fails otherwise, by producing either a tight fit to an incorrect solution, or by computing a solution which includes outliers. We thus propose a new estimator which enforces stability of the solution with respect to the uncertainty bound. We show that the variance of the estimated parameters (VoP) exhibits ranges of stability with respect to this bound. Within this range of stability, we can accurately segment the inliers, and estimate the parameters, the variance of the Gaussian noise. We show how to compute this stable range using RANSAC and a search. We validate our results by extensive tests and comparison with state of the art estimators on both synthetic and real data sets. These include line fitting, homography estimation, and fundamental matrix estimation. The proposed method outperforms all others. |
Starting Page | 675 |
Ending Page | 682 |
File Size | 444991 |
Page Count | 8 |
File Format | |
ISBN | 9781424439928 |
ISSN | 10636919 |
DOI | 10.1109/CVPR.2009.5206678 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2009-06-20 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Parameter estimation Uncertainty Stability Computer vision Intelligent robots Gaussian noise Intelligent systems Testing State estimation Application software |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition Software |
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