On achievable accuracy in edge localization
Kakarala, R.
Hero, A.O.
Dept. of Math., California Univ., Irvine, CA;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Jul 1992
Volume: 14,
Issue: 7
On page(s): 777-781
ISSN: 0162-8828
References Cited: 19
CODEN: ITPIDJ
INSPEC Accession Number: 4241511
Digital Object Identifier: 10.1109/34.142913
Current Version Published: 2002-08-06
Abstract
Edge localization occurs when an edge detector determines the
location of an edge in an image. The authors use statistical parameter
estimation techniques to derive bounds on achievable accuracy in edge
localization. These bounds, known as the Cramer-Rao bounds, reveal the
effect on localization of factors such as signal-to-noise ratio (SNR),
extent of edge observed, scale of smoothing filter, and a priori
uncertainty about edge intensity. By using continuous values for both
image coordinates and intensity, the authors focus on the effect of
these factors prior to sampling and quantization. They also analyze the
Canny algorithm and show that for high SNR, its mean squared error is
only a factor of two higher than the lower limit established by the
Cramer-Rao bound. Although this is very good, the authors show that for
high SNR, the maximum-likelihood estimator, which is also derived,
virtually achieves the lower bound
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