平成 27 年度電気関係学会四国支部連合大会 講演論文集 (2015 高知工科大学)
2015 SHIKOKU-SECTION JOINT CONVENTION RECORD OF THE INSTITUTES OF ELECTRICAL AND RELATED ENGINEERS (KOCHI)

1-3
Edge Detection by Using Switching System withCellular Neural Networks and Delayed Cellular Neural Networks
Taro NAKANO

Yoko UWATE
Yoshifumi NISHIO(Tokushima University)

1. Introduction

Edge detection templates of the DCNN :In our previous study, we applied the DCNN to image
processing and obtained good results. However, we havea problem of the noise in image processing. We consider
past information of the DCNN have a profound eﬀect.
In this study, we propose a new algorithm of switchingsystem with the conventional CNN and the DCNN. Our
proposed method is switched the conventional CNN andthe DCNN by iteration values. Therefore, our proposed
method is less past influence than the DCNN. In order
to confirm the eﬀectiveness of the proposed method, weinvestigate the eﬀect of the proposed method in image
processing.

A=0
 00

D=01
0−0.1
 −0.1−0.1

0−10  , B =  −1
0−1−0.1
0.1
−0.1



−1
8−1−1−1  ,−1−0.1−0.1  , T = −1.
−0.1

(2)

2. Proposed method
In this section, we explain the algorithm of our proposed
method. The proposed method is switched with the conventional CNN and the DCNN by iteration values. Thealgorithm is described as follows.

(a)

(b)

(c)

(d)

[Step1]: We set the parameter of iteration values τ1 andτ2 . In the parameter of iteration values, τ1 is less than τ2 .
[Step2]: We select the conventional CNN or the DCNN
by iteration values τ1 and τ2 . Figure 1 shows the switchingrule of the proposed method. If the iteration value is range
0 to τ1 or more than τ2 , we select the conventional CNN .
On the other hand, if the iteration value is range τ1 to τ2 ,we select the DCNN .

Figure 2: Simulation results of LENNA 256×256. (a) Input image. (b) Simulation result of the conventional CNN. (c) Simulation
result of the DCNN(τ = 0.05). (d) Simulation result of the proposed method (τ1 = 0.1 and τ2 = 4).

Figure 2 shows simulation results of the edge detection. Figure 2(a) shows an input image. Indistinct parts
are a pillar and mirror of the background. Figure 2(b)shows the simulation result of the conventional CNN. InFig. 2(b), the conventional CNN detects less edge lines ofindistinct parts. Figure 2(c) shows the simulation resultsof the DCNN (τ = 0.05). In Fig. 2(c), the DCNN detectsedge lines of indistinct parts. However, the processing of
the DCNN have a problem of the noise. Figure 2(d) shows
simulation result of the proposed method (τ1 = 0.1 and τ2= 4). In Fig. 2(d), the proposed method detects edge linesof indistinct parts. Moreover, the proposed method solves
the problem of noise. Therefore, the proposed method ismore eﬀective than the conventional CNN and the DCNN
in the edge detection.

Figure 1: Switching Rule.

3. Simulation results
In this section, in order to confirm the eﬀectiveness ofthe proposed method, we investigate the proposed methodin the edge detection. We show some simulation resultsof the edge detection. Edge detection templates of theconventional CNN and the DCNN are described as follows.
Edge detection templates of the conventional CNN :A=0 00
B=−1
 −1−1010



00 ,
0−1
8−14. Conclusions
In this study, we proposed the new algorithm of switching system with the conventional CNN and the DCNN. Inorder to confirm the eﬀectiveness of the proposed method,
we investigate the eﬀect of the proposed method in edge
detection. From some simulation results, the proposed
method is more eﬀective than the conventional CNN andthe DCNN in the edge detection.

−1−1  , T = −1.
−1(1)3