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 
2FP
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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.