平成22年度電気関係学会四国支部連合大会

331

18-32
Cellular Neural Networks with Switching Templatefor Edge Detection
Yoshihiro KATO Yasuhiro UEDA Masakazu KAWAHARA
Yoko UWATE Yoshifumi NISHIO(Tokushima University)

1. Introduction
Cellular Neural Networks (CNN) were introduced byChua and Yang in 1988 [1]. The idea of the CNN was inspired from the architecture of the cellular automata andthe neural networks. The CNN has local connectivity property. Wiring weights of the cells are established by parameters called the template.
In the conventional CNN, using edge detection templateof 3×3 matrix, the edge of the indistinct portion has notbeen detected. On the other hand, using edge detection
template of 5×5 matrix, detected edge becomes bold line.
In this study, we propose CNN with switching templatefor edge detection. The template of our proposed method
is switched by the output value of the corresponding cell.
We show effective edge detection from simulation results.

Edge detection
templateof¸·· 3 × 3 matrix ¸:A3 =000010000=

 00
, B3 =

−1−1−1−1
8−1−1−1−1, I = −1.

(4)

 , I = −1.
(5)Edge detection template of 5 × 5 matrix :0A500

 −1B5=−1−1−1−1000000010000000

−100
0
−1−10
160−100000,
−1000−1−1−1−1−1−1

2. Proposed Method
In this section, we explain the algorithm of the proposed
method. In our method, 2 types of template are switchedby the value of cell.
Step1 : First, we determine the boundary values D1 andD2 . If Vi is over D1 and under D2 , the template of 5×5matrix is used. On the other hand, if Vi is under D1 orover D2 , the template of 3×3 matrix is used.
Step2 : The state equation and the output equation aredescribed as follows.

(a)

(b)

(c)

State equation with the template of 3×3 matrix :dvxij
dt

j+r
i+r
X
X=−vxij +A3 (i, j; k, l)vxkl (t)(d)

result using template of 3×3 matrix. (c) Edge detection result usingtemplate of 5×5 matrix. (d) Edge detection result using CNN withswitching template. (e) Distribution of template of 5×5 matrix andtemplate of 3×3 matrix.

j+ri+rX
X+B3 (i, j; k, l)vukl (t) + I.
k=i−r l=j−r(|i − k| ≤ 1 , |j − l| ≤ 1).

(1)

State equation with the template of 5×5 matrix :dvxij
dtj+ri+rX
X=−vxij +A5 (i, j; k, l)vxkl (t)k=i−r l=j−rj+ri+r
XXB5 (i, j; k, l)vukl (t) + I.

+k=i−r l=j−r(|i − k| ≤ 2 , |j − l| ≤ 2).

(2)

Output equation :vyij (t)=(e)

Figure 1: Simulation result. (a) Input image. (b) Edge detection

k=i−r l=j−r1(|vxij (t) + 1| − |vxij (t) − 1|).
2

(3)Then, state values and output values are updated.
Step3 : After Step2, the proposed CNN switches the template to 3×3 matrix or 5×5 matrix according to the output
value of the corresponding cell.
We carry out Step2 and Step3 every 0.5[τ ].

3. Simulation Results
In this section, we show a simulation result using ourproposed method. In this simulation, D1 and D2 are set
to −0.3 and 0.1, respectively.

The pillar on the left side in the background is an indistinct portion in Fig. 1(a). This indistinct portion is notdetected using the conventional CNN in Fig. 1(b). Although, the edge of the indistinct portion is detected inFig. 1(c), the detected edge becomes bold line. In our proposed method, the edge of the indistinct portion can bedetected more clearly than using the template of 3×3 matrix. Also, the detected edge is more fine line than using
the template of 5×5 matrix. In Fig. 1(e), black points
show the cells using the template of 3×3 matrix and white
points show the cells using the template of 5×5 matrix.
From Fig. 1(e), distinct parts are processed by the template of 3×3 matrix. On the other hand, indistinct parts
are processed by the template of 5×5 matrix. From theseresults, we can say that our proposed method is more effective than the conventional CNN.

4. Conclusions

In this study, we have proposed CNN with switching
template for edge detection. In our proposed method, 2types of template were switched by the value of cell. We
confirmed from simulation results that our proposed
method is effective for edge detection.
References[1] L. O. Chua and L. Yang, “Cellular Neural Networks:Theory,”
IEEE Trans. Circuits Syst., vol. 32, pp. 1257-1272, Oct. 1988.