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

Mohammed BENNAMOUN; Boualem BOASHASH

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

Mohammed BENNAMOUN, Boualem BOASHASH

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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 suppresion 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, σ²_n. The results were applied to the 2D case and the performance of the adaptive hybrid detector was compared to other detectors.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E80-A No.8 pp.1423-1429

Publication Date: 1997/08/25

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Type of Manuscript: Special Section PAPER (Special Section on Digital Signal Processing)

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Mohammed BENNAMOUN, Boualem BOASHASH, "A Probabilistic Approach for Automatic Parameters Selection for the Hybrid Edge Detector" in IEICE TRANSACTIONS on Fundamentals, vol. E80-A, no. 8, pp. 1423-1429, August 1997, doi: .
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 suppresion 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, σ²_n. The results were applied to the 2D case and the performance of the adaptive hybrid detector was compared to other detectors.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e80-a_8_1423/_p

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@ARTICLE{e80-a_8_1423,
author={Mohammed BENNAMOUN, Boualem BOASHASH, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Probabilistic Approach for Automatic Parameters Selection for the Hybrid Edge Detector},
year={1997},
volume={E80-A},
number={8},
pages={1423-1429},
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 suppresion 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, σ²_n. The results were applied to the 2D case and the performance of the adaptive hybrid detector was compared to other detectors.},
keywords={},
doi={},
ISSN={},
month={August},}

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TY - JOUR
TI - A Probabilistic Approach for Automatic Parameters Selection for the Hybrid Edge Detector
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1423
EP - 1429
AU - Mohammed BENNAMOUN
AU - Boualem BOASHASH
PY - 1997
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E80-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 1997
AB - 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 suppresion 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, σ²_n. The results were applied to the 2D case and the performance of the adaptive hybrid detector was compared to other detectors.
ER -