Detection of image structures using the Fisher information and the Rao metric
Maybank, Stephen J. (2004) Detection of image structures using the Fisher information and the Rao metric. IEEE Transactions on Pattern Analysis and Machine Intelligence 26 (12), pp. 1579-1589. ISSN 0162-8828.
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.
|Additional Information:||This is an exact copy of a paper published in IEEE Transactions on Pattern Analysis and Machine Intelligence (ISSN 0162-8828). It is reproduced with permission from the publisher. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. © 2004 IEEE. Copyright and all rights therein are retained by authors or by other copyright holders. All persons downloading this information are expected to adhere to the terms and constraints invoked by copyright. This document or any part thereof may not be reposted without the explicit permission of the copyright holder.|
|Keyword(s) / Subject(s):||analysis of algorithms, clustering, edge and feature detection, multivariate statistics, robust regression, sampling, search process|
|School:||Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems|
|Date Deposited:||21 Feb 2006|
|Last Modified:||17 Apr 2013 12:32|
Additional statistics are available via IRStats2.