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Detection, localization, and estimation of edges
February 1989 (vol. 11 no. 2)
pp. 191,192,193,194,195,196,197,198
ASCII Text
x 
 
"Detection, localization, and estimation of edges," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 2, pp. 191,192,193,194,195,196,197,198, February, 1989.
 
 
BibTex
x
 
@article{ 10.1109/34.16714,
author = {},
title = {Detection, localization, and estimation of edges},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {11},
number = {2},
issn = {0162-8828},
year = {1989},
pages = {191,192,193,194,195,196,197,198},
doi = {http://doi.ieeecomputersociety.org/10.1109/34.16714},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Detection, localization, and estimation of edges
IS - 2
SN - 0162-8828
SP
EP
EPD - 191,192,193,194,195,196,197,198
PY - 1989
KW - picture processing
KW - filtering and prediction theory
KW - iterative methods
KW - pattern recognition
KW - zero crossings
KW - edge estimation
KW - edge detection
KW - edge localisation
KW - Laplacian of Gaussian filter
KW - pattern recognition
KW - picture processing
KW - Image edge detection
KW - Image reconstruction
KW - Convergence
KW - Iterative methods
KW - Filters
KW - Concurrent computing
KW - Convolution
KW - Biology computing
KW - Physics computing
KW - Layout
VL - 11
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
A method to detect, locate, and estimate edges in a one-dimensional signal is presented. It is inherently more accurate than all previous schemes as it explicitly models and corrects interaction between nearby edges. The method is iterative with initial estimation of edges provided by the zero crossings of the signal convolved with Laplacian of Gaussian (LoG) filter. The necessary computations necessitate knowledge of this convolved output only in a neighborhood around each zero crossing and in most cases, could be performed locally by independent parallel processors. Results on one-dimensional slices extracted from real images, and on images which have been proposed independently in the row and column directions are shown. An analysis of the method is provided including issues of complexity and convergence, and directions of future research are outlined.<>

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Index Terms:
picture processing,filtering and prediction theory,iterative methods,pattern recognition,zero crossings,edge estimation,edge detection,edge localisation,Laplacian of Gaussian filter,pattern recognition,picture processing,Image edge detection,Image reconstruction,Convergence,Iterative methods,Filters,Concurrent computing,Convolution,Biology computing,Physics computing,Layout
Citation:
"Detection, localization, and estimation of edges," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 2, pp. 191,192,193,194,195,196,197,198, Feb. 1989, doi:10.1109/34.16714
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