Optimal parameters for edge detection

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Optimal parameters for edge detection

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dc.contributor.author Bennamoun, M
dc.contributor.author Boashash, B
dc.contributor.author Koo, J
dc.date.accessioned 2011-09-28T17:55:10Z
dc.date.available 2011-09-28T17:55:10Z
dc.date.issued 1995-10
dc.identifier.citation Systems, Man and Cybernetics, 1995. Intelligent Systems for the 21st Century., IEEE International Conference on Issue Date : 22-25 Oct 1995 Volume : 2 On page(s): 1482 en_US
dc.identifier.isbn 0-7803-2559-1
dc.identifier.uri http://hdl.handle.net/10576/10744
dc.description This paper presents an edge detection method, a key component for many image processing techniques used in pattern recognition, robot vision, stereo vision, segmentation, feature extraction, compression. The method present is robust against noise independently of the scene (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). en_US
dc.description.abstract Bennamoum, and Masoud, Bennamoum and Bayoumi (1991), suggested a robust edge detector which relaxes the trade-off between robustness 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, σn2. The results were applied to the 2D case and the performance of the adaptive hybrid detector was compared to other detectors. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.subject localization en_US
dc.subject noise robustness en_US
dc.subject noise suppression en_US
dc.subject noise variance en_US
dc.subject optimal parameters en_US
dc.subject probabilistic cost function optimization en_US
dc.subject robust edge detector en_US
dc.subject sub-detectors en_US
dc.subject hybrid edge detector en_US
dc.subject parameter selection en_US
dc.subject image processing en_US
dc.subject vision en_US
dc.title Optimal parameters for edge detection en_US
dc.type Article en_US

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