A Probabilistic Approach for Automatic Parameters Selection for the Hybrid Edge Detector

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A Probabilistic Approach for Automatic Parameters Selection for the Hybrid Edge Detector

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dc.contributor.author Bennamoun, M
dc.contributor.author Boashash, B
dc.date.accessioned 2012-06-18T14:22:36Z
dc.date.available 2012-06-18T14:22:36Z
dc.date.issued 1997-08
dc.identifier.citation M. Bennamoun and B. Boashash, “A Probabilistic Approach for Automatic Parameters Selection for the Hybrid Edge Detector”, Special Section on Digital Signal Processing in the IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, Vol. E80-A, No. 8, pp.1423-1429, August, 1997 en_US
dc.identifier.uri http://hdl.handle.net/10576/10850
dc.description This paper describes an adaptive probabilistic technique to automatically select the parameters of a hybrid, first and second order differential edge detector independently of the scene. (Additional details for non-stationary signals can be found in the comprehensive book on Time-Frequency Signal Analysis and Processing (see http://www.elsevier.com/locate/isbn/0080443354). In addition, the most recent upgrade of the original software package that calculates Time-Frequency Distributions and Instantaneous Frequency estimators can be downloaded from the web site: www.time-frequency.net. This was the first software developed in the field, and it was first released publicly in 1987 at the 1st ISSPA conference held in Brisbane, Australia, and then continuously updated).
dc.description.abstract We previously proposed a robust hybrid edge detector which relaxes the trade off between robustess against noise and accurate localization of the edges. This hybrid detector separates the tasks of localization and noise suppression between two sub-detectors. In this paper, we present an extension to this hybrid detector to determine its optimal parameters, independently of the scene. This extension defines a probabilistic cost function using for criteria the probability of missing an edge buried in noise and the probability of detecting false edges. The optimization of this cost function allows the automatic selection of the parameters of the hybrid edge detector given the height of the minimum edge to be detected and the variance of the noise, CT~. The results were applied to the 20 case and the performance of the adaptive hybrid detector was compared to other detectors. en_US
dc.language.iso en en_US
dc.publisher IEICE en_US
dc.subject Pattern recognition en_US
dc.subject vision systems en_US
dc.subject edge detection en_US
dc.subject parameter selection en_US
dc.subject Gaussian filter en_US
dc.subject zero-crossing en_US
dc.subject probability en_US
dc.subject cost function en_US
dc.subject Pattern recognition
dc.subject vision systems
dc.subject edge detection
dc.subject parameter selection
dc.subject Gaussian filter
dc.subject zero-crossing
dc.subject probability
dc.subject cost function
dc.subject optimal filtering
dc.subject hybrid edge detector
dc.title A Probabilistic Approach for Automatic Parameters Selection for the Hybrid Edge Detector en_US
dc.type Article en_US

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