平成25年度電気関係学会四国支部連合大会 講演論文集 （2013 徳島大学）
2013 SHIKOKU-SECTION JOINT CONVENTION RECORD OF THE INSTITUTES OF ELECTRICAL AND RELATED ENGINEERS (TOKUSHIMA)

1-20
Alternative Arrangement Cellular Neural Networksfor Image Processing Applications
Tatsuya MASUOKA

Kazushige NATSUNO Yoko UWATE Yoshifumi NISHIO(Tokushima University)

1. Introduction

3. Simulation Results

In this study, we propose a new model of CNN. Thismodel has set up by turns two templates which connection of cells is diﬀerent. Cellular neural network spatially
uniform can not perform complex processing. However,implementation cost of proposed model is same as conventional CNN. We proposed the model which mounting cost
is comparatively cheap and conformation is easy to basic
image processing applications. And we observed good results even if it is not a complicated structure.

In this section, we show simulation results of edge detection. The pillar on the left side in the background isan indistinct portion in Fig. 2(a). This indistinct portion
is not detected by using the conventional CNN in Fig.
2(b). Although, the edge of the indistinct portion is detected in Fig. 2(c), the detected edge becomes bold line.
In Fig 2(d), the edge of the indistinct portion can be detected more clearly than the Fig 2(c). However, detectionof indistinct portion is not enough. Therefore, the double
value of the β template as shown in Eq. (4), it is resultedin Fig. 2(e). By using proposed model, noise is less thanusing the template of 5 × 5 matrix. From these results,
we can say that our proposed model is more eﬀective thanthe conventional CNN.

2. Proposed ModelIn this section, we explain the structure of the proposedmodel. The proposed model has two templates that is
diﬀerent characteristic in Figs.1(a) and (b). In particular,
two templates are alternately set up as shown in Fig 1(c).
A α template is placed in a black cell, and a β templateis placed in a diagonal cells.

(a)

(a)

(b)

(b)

(c)

(c)

Figure 1: (a) Connection structure of the α template. (b)Connection structure of the β template. (c) Template placement assignment.

(d)
In addition, cells are coupled to the speciﬁed eight surrounding cells. The values of each cell are determined by
the state and the output equations as follows.

template of 3 × 3 matrix. (c) Edge detection result using template of 5 × 5 matrix. (d) Edge detection result using proposedmodel. (e) Double value of the β template.

State equation of the α template：
i+rα
∑
dvxij
= −vxij +dt

∑

j+rα

Aα (i, j; k, l)vykl (t)α=k=i−rα l=j−rα

∑

i+rα+

∑

j+rα

Bα (i, j; k, l)vukl (t) + Iα .

dvxij
= −vxij +dt∑

i+rβ+∑i+rβ∑

β=j+rβAβ (i, j; k, l)vykl (t)

−1
 0
 0
 0−10−10−10008000−10−10−10 0 
0 −10
 0
 −2
 000
0−20
0−2−2
16−2−200−20000 

−2 
0 0



(1)

k=i−rα l=j−rαState equation of the β template：(e)Figure 2: (a) Input image. (b) Edge detection result using,(4)k=i−rβ l=j−rβ∑

4. Conclusions

j+rβBβ (i, j; k, l)vukl (t) + Iβ .

(2)k=i−rβ l=j−rβOutput equation：
1vyij (t) = (|vxij (t) + 1| − |vxij (t) − 1|).
2

(3)

20

In this study, we have proposed a new model of CNN.
This model has set up by turns two templates which connection of cells is diﬀerent. When we set up alternately
template and change the value of the parameter, the performance of the proposed CNN has improved.