| 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 |