平成 29 年度電気関係学会四国支部連合大会 講演論文集 (2017 愛媛大学)
2017 SHIKOKU-SECTION JOINT CONVENTION RECORD OF THE INSTITUTES OF ELECTRICAL AND RELATED ENGINEERS (EHIME)

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Cellular Neural Networks with Partial Delay Outputfor Edge Detection
Fumiki Sugano

Takahisa Ando Yoko Uwate(Tokushima University)

Yoshifumi Nishio

1. Introduction
Cellular Neural Networks with Delay Output (DCNN)
has been applied in the time-varying image processing, however it was not applied in the still image processing. Sincevideo is continuous of images, we considered that the DCNN
could be applied to images.
In the previous study, it is reported that the DCNN wasapplied to the image processing and obtained good results.
However, it has problem that noise effect is observed in theoutput image.
In this study, we propose a method of CNN with partial
delay output (DCNN). We investigate the performance ofthe proposed method in edge detection.

(a)

(b)

(c)

(d)

2. Proposed Method
In this section, we explain the algorithm of the proposedmethod. The proposed method is CNN with partial delayoutput (DCNN). The algorithm is described as follows.
[Step1]: Set parameter of a and N .
−1 ≤ a ≤ 1, 0 ≤ N ≤ 8.

Figure 1: Simulation results. (a) Input image. (b) Simula-(1)tion result of the conventional CNN. (c) Simulation result of theDCNN(τ =0.01). (d) Simulation result of the proposed method(a = 0.00017, x = 5).

[Step2]: Calculate difference values between the output
value of the center cell and the output values of
the neighboring cells.

In Fig. 1, we show the input image and the simulation
results of the edge detection. Figure 1(a) shows the input
image. In the input image, indistinct parts are the right sideof the red pepper and the vegetable in the upper left. InFig. 1(b), we show the simulation result of the conventional
CNN. The conventional CNN cannot detect edge lines ofthe indistinct parts. In Fig. 1(c), we show the simulation
result of the DCNN (τ =0.01). The DCNN can detect edgelines of indistinct parts, however, remains the noise effect.
In Fig. 1(d), we show the simulation result of the proposed
method (a=0.00017, x=5). The proposed method can detect edge lines of indistinct parts and receive less noise effect compared with the DCNN. It is because we applied theDCNN to indistinct parts.

[Step3]: Count number of cells which have the difference
value smaller than a. Then, determine the method
according to the following equations.

CN N : x > N
DCN N : x ≤ N(2)3. Simulation Results
In this section, we apply the proposed method to edge
detection. Edge detection templates of the conventional
CNN and the DCNN are described as follows.
Edge detection templates of the conventional CNN :0 0 0A =  0 1 0 ,0 0 0



4. Conclusions−1 −1 −1B =  −1(3)8−1  , I = −1.
−1 −1 −1
Edge detection templates of the DCNN :
0 0 0−1 −1 −1
A =  0 1 0  , B =  −1
8−1  ,
0 0 0−1 −1 −1

D

=−0.1
 −0.1−0.1−0.1
0.1−0.1

In this study, we have proposed a new CNN method of
adding delay output (DCNN) to the conventional CNN partially. From the simulation results, the proposed method
can detect edge lines of indistinct parts and decrease effectof noise compared with the DCNN. Therefore, the proposed
method is more effective than the conventional CNN andthe DCNN in the edge detection. In the future work, wewill investigate the performance of the proposed method foranother image processing.

−0.1−0.1  , I = −1.
−0.1(4)10