A Cellular Neural Network- Based Model for Edge Detection
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@Article{JICS-5-003,
author = {Hezekiah Babatunde, Olusegun Folorunso and Adio Akinwale},
title = {A Cellular Neural Network- Based Model for Edge Detection},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {5},
number = {1},
pages = {003--010},
abstract = { This study employed the use of Cellular Neural Networks (CNN) for Edge Detection in images
due to its high operational speed. The process of edge detection is unavoidable in many image processing
tasks such as obstacle detection and satellite picture processing .The conventional edge detector models such
as Sobel Operator, Robert Cross, among which Canny is the best, have high computational time. The CNN
Model is a class of Differential Equation that has been known to have many application areas and high
operational speed. The work investigated four parameters: resolutions, processing time, false alarm rate, and
usability for performance evaluation. The CNN Model was modified by using hyperbolic tangent (tanh x)
and Von Neumann Neighborhood. The modified CNN Model and enhanced Canny Model were implemented
using MATLAB 7.0 running on Pentium III and 128 MB RAM Personal Computer. A series of images
served as input for both Canny and modified CNN Model. With several images tested, the overall results
indicated that the two models have similar resolutions with average computational time of 1.1078 seconds
and 2.293 seconds for CNN-based and Enhanced Canny Model respectively. The hyperbolic entry a22 of the
cloning template A made our work fully controllable since max(tanh x) = +1 and min(tanh x) = -1. A
consideration of the set of digital images showed that edge maps which result from Canny Model have
adjacent boundaries that tend to merge. The CNN model obviously produced the optimum edge map with
2FP
edges that are one-pixel wide and unbroken. The false alarm rate of noise variance probability
for which an edge detector can easily declare an edge pixel given that there is no edge, showed the value
0.8525 for CNN Model and 0.4595 for Canny Model. The CNN parameters can be adjusted and modeled to
solve Partial Differential Equations and Maximum likelihood problems for edge detection while Canny
Model cannot be easily adjusted for any other functionality. The CNN-based edge detector performed better
than the popular canny operator in terms of the computational time required, usability, and false alarm rate.
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22724.html}
}
TY - JOUR
T1 - A Cellular Neural Network- Based Model for Edge Detection
AU - Hezekiah Babatunde, Olusegun Folorunso and Adio Akinwale
JO - Journal of Information and Computing Science
VL - 1
SP - 003
EP - 010
PY - 2024
DA - 2024/01
SN - 5
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jics/22724.html
KW - Cellular Neural Networks, Canny Model, Edge Detection, hyperbolic tangent, Von Neumann
Neighborhood
AB - This study employed the use of Cellular Neural Networks (CNN) for Edge Detection in images
due to its high operational speed. The process of edge detection is unavoidable in many image processing
tasks such as obstacle detection and satellite picture processing .The conventional edge detector models such
as Sobel Operator, Robert Cross, among which Canny is the best, have high computational time. The CNN
Model is a class of Differential Equation that has been known to have many application areas and high
operational speed. The work investigated four parameters: resolutions, processing time, false alarm rate, and
usability for performance evaluation. The CNN Model was modified by using hyperbolic tangent (tanh x)
and Von Neumann Neighborhood. The modified CNN Model and enhanced Canny Model were implemented
using MATLAB 7.0 running on Pentium III and 128 MB RAM Personal Computer. A series of images
served as input for both Canny and modified CNN Model. With several images tested, the overall results
indicated that the two models have similar resolutions with average computational time of 1.1078 seconds
and 2.293 seconds for CNN-based and Enhanced Canny Model respectively. The hyperbolic entry a22 of the
cloning template A made our work fully controllable since max(tanh x) = +1 and min(tanh x) = -1. A
consideration of the set of digital images showed that edge maps which result from Canny Model have
adjacent boundaries that tend to merge. The CNN model obviously produced the optimum edge map with
2FP
edges that are one-pixel wide and unbroken. The false alarm rate of noise variance probability
for which an edge detector can easily declare an edge pixel given that there is no edge, showed the value
0.8525 for CNN Model and 0.4595 for Canny Model. The CNN parameters can be adjusted and modeled to
solve Partial Differential Equations and Maximum likelihood problems for edge detection while Canny
Model cannot be easily adjusted for any other functionality. The CNN-based edge detector performed better
than the popular canny operator in terms of the computational time required, usability, and false alarm rate.
Hezekiah Babatunde, Olusegun Folorunso and Adio Akinwale. (2024). A Cellular Neural Network- Based Model for Edge Detection.
Journal of Information and Computing Science. 5 (1).
003-010.
doi:
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