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)

1-34
Image Processing Using Cellular Neural Networks
with Changing Templates
Shohei GOTODA Taro NAKANO Yoko UWATE Yoshifumi NISHIO(Tokushima University)

1. Introduction
A template is important in image processing of the Cellular Neural Networks (CNN). The template is unchangeable
spatially and temporally. Therefore, the convetional CNN
can not perform complex processing.
In this study, we propose a new method of changing templates by using pseudorandom numbers. In order to confirm
the eﬀectiveness of the proposed method, we investigate theproposed method in image processing.

(a)

(b)

(c)

(d)

2. Proposed Method
The proposed method changes templates in pseudorandom numbers. We add pseudorandom numbers α to a template A, and add pseudorandom numbers β to a template
B. Pseudorandom numbers are added every the number of
constant processing times Tp . The templates of the proposed method show Eq.(1),The templates of proposed method :A=

a12 + αa22 + α
am2 + αa1n + αa2n + α 
amn + αb11 + β b21 + β
bm1 + βb12 + βb22 + βbm2 + βb1n + βb2n + β bmn + βB=a11 + α a21 + α
am1 + αFigure 1: Simulation results. (a) Input image. (b) Simulation result of the conventional CNN. (c) Simulation result of
the proposed method (α = -0.9 ∼ 0, β = -0.01 ∼ 0, Tp = 10).
(d)Simulation result of the proposed method (α = -0.9 ∼ 0 , β= -0.01 ∼ 0, Tp = 5 ).

(1)Figure 1(a) is an input image. The input image has indistinct portions. Indistinct portions are the pillar of theleft-side and an outline of a woman’s face. Figure 1(b)shows the simulation result of the conventional CNN. The
conventional CNN can not detect edge lines of indistinct
portions. Figure 1(c) shows the simulation result of the proposed method(Tp = 10 ). In Fig. 1(c), the proposed method
cannot detect edge lines of indistinct portions. Figure 1(d)shows the simulation result of the proposed method (Tp =5 ). In Fig. 1(d), the proposed method can detect edgelines of indistinct portions. We confirm that the proposed
method performs well in image processing when we set theparameter Tp appropriately. From the simulaton results,
the proposed method is more eﬀective than the conventional
CNN.

In this study, we add pseudorandom numbers to edge detection template. The parameters of pseudorandom numbers
are fixed with α = -0.9 ∼ 0, β = -0.01 ∼ 0.

3. Simulation Results
In this section, we show simulation results of the edge
detection by using our proposed method. Using templatesof the edge detection is described as follows.
Edge detection template :

A=0
 00
B=−1
 −1−1010

00 ,
0−18−1−1−1  , T = −1.
−1(2)4. Conclusions
In this study, we propose the new method of changing
templates by pseudorandom numbers. In order to confirm
the eﬀectiveness of the proposed method, we investigate
the proposed method in image processing. As a result, theproposed method is more eﬀective than the conventional
CNN in image processing.

Edge detection template of the proposed method :A=000000 ,
0−1 + β −1 + β−1 + β−1 + β8+β−1 + β 0 1+αB=−1 + β−1 + β  , T = −1. (3)−1 + β34