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Detection of Image Structures Using the Fisher Information and the Rao Metric
December 2004 (vol. 26 no. 12)
pp. 1579-1589
ASCII Text
x 
 
Stephen J. Maybank, "Detection of Image Structures Using the Fisher Information and the Rao Metric," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 12, pp. 1579-1589, December, 2004.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2004.122,
author = {Stephen J. Maybank},
title = {Detection of Image Structures Using the Fisher Information and the Rao Metric},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {26},
number = {12},
issn = {0162-8828},
year = {2004},
pages = {1579-1589},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2004.122},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Detection of Image Structures Using the Fisher Information and the Rao Metric
IS - 12
SN - 0162-8828
SP1579
EP1589
EPD - 1579-1589
A1 - Stephen J. Maybank,
PY - 2004
KW - Analysis of algorithms
KW - clustering
KW - edge and feature detection
KW - multivariate statistics
KW - robust regression
KW - sampling
KW - search process.
VL - 26
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
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.

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Index Terms:
Analysis of algorithms, clustering, edge and feature detection, multivariate statistics, robust regression, sampling, search process.
Citation:
Stephen J. Maybank, "Detection of Image Structures Using the Fisher Information and the Rao Metric," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 12, pp. 1579-1589, Dec. 2004, doi:10.1109/TPAMI.2004.122
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