平成 28 年度電気関係学会四国支部連合大会 講演論文集 (2016 徳島大学)
2016 SHIKOKU-SECTION JOINT CONVENTION RECORD OF THE INSTITUTES OF ELECTRICAL AND RELATED ENGINEERS (TOKUSHIMA)

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Cellular Neural Networks with Switching
Two-Type Templates for Edge Detection
Takahisa Ando, Yoko Uwate and Yoshifumi Nishio(Tokushima University)

1. Introduction
In this study, we propose switching two-type templates
CNN. In our proposed method, 3 × 3 template and 5 ×5 template are switched. When 3 × 3 template is used,
processing speed is quick, however the delicate process isdifficult. When 5 × 5 template is used, processing speed isbrady and easy to receive noise effect, however the delicate
process is possible. We investigate this method in edge
detection.

(a)

(b)

2. Proposed Method
In our proposed method, two templates are switched according to the output values around cells in processing.
Switching condition depends on the difference value between the maximum and minimum output values ymax ,ymin of each 5 × 5 neighborhood and is given as follows:|ymax − ymin | ≤ a|ymax − ymin | > a(1)We fix a certain value a as threshold about switching
template. When the top inequality of (1) is satisfied , 3 × 3
template is used. In the other case, 5 × 5 template is used.
This switching process is conducted every certain numberof calculation n.

3. Simulation Results
In this section, we show simulation results of the edge
detection for using our proposed method and the conventional CNN. Using 3 × 3 and 5 × 5 templates of the edge
detection is described as follows.
Edge detection template :A=0 00B==B

=



−1
8−100000001000000000 0 ,0 
0−100
0−1−10160−1−1000−10
 0
 0
 00



00 ,0−1
 −1−1

A



0
10−1
 −1

 −1
 −1−1−1−1  , I = −1.
−1(2)





−1−1 

−1 
 , I = −1. (3)−1 −1(c)(d)
Figure 1: Simulation results. (a) Input image. (b) Simulation
result of the 3 × 3 CNN. (c) Simulation result of the 5 × 5 CNN.
(d)Simulation result of the proposed method (a = 0.8, n = 60).

Figure 1(a) shows 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 3×3 CNN. The 3×3 CNN
cannot detect edge lines of indistinct portions. Figure 1(c)shows the simulation result of the 5×5 CNN. The 5×5 CNN
can detect edge lines of indistinct portions, however image
processed by this method receives noise effect. Figure 1(d)shows the simulation result of our proposed method (a =0.8, n = 60). In Fig. 1(d), our proposed method can detectedge lines of indistinct portions and receive less noise effectcompared to Fig. 1(c). From the simulation results, ourproposed method is more effective than the conventional
CNN.

4. Conclusions
In this study, we proposed the new method of switching two-type templates by the threshold a and the certain
number of calculation n for CNN. In order to confirm theeffectiveness of our proposed method, we tried our proposed
method as a method of edge detection. As a result, our proposed method is more effective than the conventional CNN
in edge detection. In future work, we will introduce thethird template as the noise removal template and switch
three-type templates.

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