NDLI: Detection of image structures using the Fisher information and the Rao metric

Content Provider	IEEE Xplore Digital Library
Author	Maybank, S.J.
Copyright Year	1979
Abstract	In many detection problems, the structures to be detected are parameterized by the points of a parameter space. If the conditional probability density function for the measurements is known, then detection can be achieved by sampling the parameter space at a finite number of points and checking each point to see if the corresponding structure is supported by the data. The number of samples and the distances between neighboring samples are calculated using the Rao metric on the parameter space. The Rao metric is obtained from the Fisher information which is, in turn, obtained from the conditional probability density function. An upper bound is obtained for the probability of a false detection. The calculations are simplified in the low noise case by making an asymptotic approximation to the Fisher information. An application to line detection is described. Expressions are obtained for the asymptotic approximation to the Fisher information, the volume of the parameter space, and the number of samples. The time complexity for line detection is estimated. An experimental comparison is made with a Hough transform-based method for detecting lines.
Sponsorship	IEEE Computer Society
Starting Page	1579
Ending Page	1589
Page Count	11
File Size	569420
File Format	PDF
ISSN	01628828
Volume Number	26
Issue Number	12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-12-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Density measurement Noise measurement Probability density function Extraterrestrial measurements Computer vision Statistics Time measurement Equations Sampling methods Upper bound search process. Index Terms- Analysis of algorithms clustering edge and feature detection multivariate statistics robust regression sampling
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Computational Theory and Mathematics Computer Vision and Pattern Recognition Software

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