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)

1-21
Two-Layer DT-CNN with Switching Template
Kazushige Natsuno, Yoko Uwate and Yoshifumi Nishio(Tokushima University)

1. Introduction

Switching template:In this study, we propose a new system of two-layer
Discrete-Time Cellular Neural Network (DT-CNN) andinvestigate the output characteristics. The proposed system has diﬀerent structure of conventional DT-CNN. In
particular, the structure of proposed system is two-layer.
Also two templates are switched by outputs value of eachlayer. From simulation results, we conﬁrm that the proposed system is more eﬀective than the conventional CNN
for some image processings.

2. Proposed system
In this section, we explain the system of the proposed
two-layer DT-CNN. Figure 1 shows the block diagram ofthe proposed system. The structure of the proposed system based on conventional two-layer CNN. Also, the proposed system has two switching templates AS and CS thatare switched by outputs value of each layer. In the twoswitching templates, AS , CS templates are switched twotypes by outputs value of each layer.

[A21=[C21=000010000000010000][, A22 =][, C22 =0000−100000000−10000]
,]
. (4)

In the Fig. 2(a), the buildings of the background areindistinct portions. Figures 2(b) and (c) show the simulation result by using conventional DT-CNN. One theother hand, Fig 2(d) shows the simulation result by using
proposed system. In the Fig. 2(b) and (c), the indistinct
portions can not be detected. In addition, in the Fig. 3(b),a detail that the camera and the aspect of face can not beshown. In contrast, in the Figs. 2(c), indistinct portions
can be detected. In addition, in the Figs. 3(c), the detailthat the camera and the aspect of face can be shown. Fromsimulation results, we can say that the proposed system
is more eﬀective than the conventional DT-CNN.

(a)

(b)

(c)

(d)

Figure 2: Simulation results. (a) Input image, (b) Binarization by conventional DT-CNN, (c) Edge detection by conventional DT-CNN, (d) First-layer of proposed system.

Figure 1: Block diagram of the proposed system.

The switching condition expression and state, output
equation are shown as follows.
Switching condition expression :vy1ij (t)=vy2ij (t) : A21 andC21 templates(1)

vy1ij (t)̸=vy2ij (t) : A22 andC22 templates

(2)

(a)

3. Simulation result
In this section, we show simulation results by using proposed system. In this simulation, we simulated edge detection and binarization. Templates and thresholds of eachlayer are assigned as follows.
Template of first-layer, coupling template and thresholds:[A1=[C12=000010000111181111][, B=]−1−1−1, T1 = T2 = 0.

−1
8−1−1−1−1]

(b)

(c)

Figure 3: Closeup of simulation results. (a) Binarization by conventional DT-CNN, (b) Edge detection
by conventional DT-CNN, (c) First-layer of proposedsystem.

4. Conclusions,(3)

21In this study, we proposed two-layer DT-CNN withswitching template. From simulation results, the proposedsystem could detect about the indistinct and the detailportions. Therefore, we could say that the proposed system is more eﬀective than the conventional DT-CNN.