Content Provider	IEEE Xplore Digital Library
Author	Krajsek, Kai Heinemann, Christian Scharr, Hanno
Copyright Year	2014
Description	Author affiliation: IBG-2: Plant Sciences, Forschungszentrum Jülich, 52425, Germany (Krajsek, Kai; Heinemann, Christian; Scharr, Hanno)
Abstract	Parameter estimation in the presence of noisy measurements characterizes a wide range of computer vision problems. Thus, many of them can be formulated as errors-in-variables (EIV) problems. In this paper we provide a closed form likelihood function to EIV problems with arbitrary covariance structure. Previous approaches either do not offer a closed form, are restricted in the structure of the covariance matrix, or involve nuisance parameters. By using such a likelihood function, we provide a theoretical justification for well established estimators of EIV models. Furthermore we provide two maximum likelihood estimators for EIV parameters, a straight forward extension of a well known estimator and a novel, local estimator, as well as confidence bounds by means of the Cramer Rao Lower Bound. We show their performance by numerical experiments on optical flow estimation, as it is well explored and understood in literature. The straight forward extension turned out to have oscillating behavior, while the novel, local one performs favorably with respect to other methods. For small motions, it even performs better than an excellent global optical flow algorithm on the majority of pixel locations.
Starting Page	270
Ending Page	279
File Size	952068
Page Count	10
File Format	PDF
ISBN	9789897581335
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-05
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	SCITEPRESS
Subject Keyword	Parameter Estimation Maximum likelihood estimation Noise Maximum Likelihood Estimation Error-in-Variables Models Optical imaging Adaptive optics CRLB Mathematical model Covariance matrices Optical Flow
Content Type	Text
Resource Type	Article

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