2014 RISP International Workshop on Nonlinear Circuits,Communications and Signal Processing
NCSP'14, Honolulu, Hawaii, USA. February 28 - March 3, 2014

Cellular Neural Network with Preference Theory for Image Processing
Tatsuya Masuoka† , Kazushige Natsuno† , Yoko Uwate† and Yoshifumi Nishio†
Tokushima University,
2-1 Minami-Josanjima,Tokushima, 770-8506 JAPAN† Email: {masuoka, natsushige, uwate, nishio}@ee.tokushima-u.ac.jp

Abstractwith preference theory (PT-CNN) incorporating psychological behavior system to the structure of the CNN.

In this study, we propose Cellular Neural Network with thePreference Theory (PT-CNN). The proposed model includes
2. Cellular Neural Network
human behavior system in the structure of cellular neural network. We apply human behavior system to the template ofCNN is the circuit having the units called cells of M × N
cellular neural network. We observe that the proposed model matrix. A circuit model of cell is shown Fig. 1. One cell isobtains intriguing results for the edge detection.
one pixel in image processing. It is composed of circuit elements of the linear and nonlinear. We show below the output
equation and the equation of state of each cell.
1. Introduction
Recent years, the amount of information which should beprocessed at once is increasing by the information society.
General digital processing is a sequential operation. The information processing time is longer, moreover it is not possible to real-time signal processing. Therefore information
process using a neural network that is designed based on thehuman nervous system has been investigated. Cellular Neural Networks (CNN) were introduced by inspired from thecellular automata and the neural networks [1]. CNN has local connectivity property and it makes the CNN tailor madefor VLSI implementation. Then, CNN has the features oftime continuity, spatial discreteness, nonlinearity and parallel
processing capability. Furthermore, the structure of CNN resembles that of animals’ retina. Therefore, CNN can be usedfor various image processing applications[2]-[4].
Coupling strength between cells of CNN is established by aparameter called the template. There is a limit to the amountof information which a cell obtain from the neighborhood.
My research group focused on sociality or actual circle offriends, and have obtained intriguing results[5][6]. Therefore,we focus on behavior system of human.
We would like to explain about preference theory. Preference theory is psychology to build a successful relationship.
In conventional psychology, human behavior is consideredtheory that is decided by the stimulus from the outside. However, human choose a best action for purpose in each case.
We personally choose own action. For example, relationshipis broken only by order when people get angry. Therefore,
we can build a successful relationship by presenting an option and negotiation. In fact, preference theory is controlled
by inside. In this study, we propose Cellular Neural Network

Figure 1: The circuit model of cell.
State equation:i+r∑dvxij= −vxij +dtj+r∑A(i,j;k,l) vykl (t)k=i−r l=j−r+i+r∑j+r∑

B(i,j;k,l) vukl (t) + I.

(1)

k=i−r l=j−r

vx represents the state value of a cell, vy represents the output value of a cell, and vu represents the input value of a cell.
In addition, A is feedback template, B is control template, I isa bias. Dynamics of CNN is determined by these values.
Output equation:vyij (t) =1(|vxij (t) + 1| − |vxij (t) − 1|).
2(2)For the output equation, output characteristic are expressedwith a piecewise linear function, moreover output value of
CNN assume continuous value from -1 to 1 in Fig. 2. Therefore, the output image is expressed as binary.
Then, we show below the block diagram in Fig. 3.
When the distance between each cell that is directly connected is k, cells is called k-neighborhood. Such composition- 141 -(a) First coupling configuration.

Figure 2: Output characteristic of CNN.

(b) Secondary coupling configuration.

Figure 4: Configuration of CNN.

of template are spatially. When we assume stimulus from theoutside as template, conventional CNN is similar inside control psychology. The flow of preference theory is Fig. 5(b).
In the proposed model, we feed choice to the cell that is applied values of the template. Therefore, the proposed model
resembles preference theory.
Figure 3: The block diagram of cell.

is called k-th joint composition. Examples of the first coupling configuration is shown in Fig. 4(a), and examples of thesecondary coupling configuration is shown in Fig. 4(b). The
area surrounded by the dotted line is neighborhood of blackcell. In other words, Fig. 4(a) shows that center cell is directly
coupled to the eight nearby cells, moreover Fig. 4(b) showsthat center cell is directly coupled to the 24 nearby cells. rneighborhood of cell is defined as:
Nr (i, j) = {C(k, l)|max{|k − i|, |l − j|} ≤ r,1 ≤ k ≤ M ; 1 ≤ l ≤ N }.

(a) The flow of outside control psychology.

(3)

3. Cellular Neural Network with the Preference Theory
CNN is processed by the information of neighboring cells.
There is a limit to the amount of information that a cell is obtained from neighboring cells. Therefore, a method to extend
a template is proposed. However, the processing is not always
sensitive. For example, a noise is detected in edge detection.
Additionally, it is necessary to set up a new template. There
are not many kinds of bigger template than 3 × 3. Thus, we
propose the model of the CNN using a combination of preference theory.
The flow of outside control psychology is Fig. 5(a). Conventional CNN is spatially uniform. Cells influenced by value(b) The flow of preference theory.

Figure 5: The flow of human behavior.

In this structure, central cell chooses automatically eightcells which propagate information from cell to cell. Even ifneighborhood is expanded, information is propagated fromonly optimal eight cells. Therefore, the 3 × 3 template can beused like Fig. 6. The proposed model choose eight cells by- 142 -most different values from a central cell. Since boundaries or and eyes are fine. The region of the right annular finger is
contours drastically change, we applied the proposed model complex.
to edge detection. In fact, this structure composes automatically an asymmetrical template. Since values differ for every 3 × 3 Edge detection template：cell, template is updated.
0 0 0−1 −1 −1
A =  0 1 0  , B =  −1 8 −1  ,0 0 0−1 −1 −1(4)I = −1.
5 × 5 Edge detection template：0 0 0 0 0
 0 0 0 0 0 A =  0 0 1 0 0 ,
 0 0 0 0 0 
0 0 0 0 0

−1 −1 −1 −1
 −1 −1 −1 −1
B = 
 −1 −1 24 −1
 −1 −1 −1 −1−1 −1 −1 −1
I = −1.

Figure 6: The flow of using 3 × 3 template on the proposed model.

−1−1 −1 
,−1 −1(5)Figures 7 to 9 show the simulation results of edge detection. The pillar on the left side in the background is an indistinct portion in Fig. 7(a). The indistinct portion is not detected by using the 3 × 3 template CNN in Fig. 7(b). In thesame way, detection is deficient in Figs. 8(b) and 9(b). Theedge of the indistinct portion is detected in Fig. 7(c) and 7(d),however excessive information is detected in Fig. 7(c). The
same is equally true of Figs. 8 and 9. We can obtain the result equivalent to to using 5 × 5 template by only informationfrom eight cells.
4. Conclusions

3.1 Edge Detection

In this study, we have proposed a new model of CNN. The
model is Cellular Neural Network with the Preference Theory(PT-CNN) which included psychological behavior system inthe structure of cellular neural network. From the simulation
results of edge detection, the proposed model is better thanthe conventional CNN.
As future works, we would like to simulate a image with ahigh pixel count since the proposed model can consider caseof a expanded neighborhood.

In this section, we show the simulation results of edge detection by the proposed model and conventional CNN. The
proposed model choose eight cells by most different values
from a central cell. We use three type of the image in edge
detection. In Fig. 7(a), the image of LENNA contains theperson area that is brought to a focus, and the backgroundarea that is not brought to a focus. The pillar of the background is indistinct. Moreover, since the shuttlecock of a hat
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Figure 8: Simulation result 2. (a) Input image (Pepper). (b) Resultof 3×3 template . (c) Result of 5×5template. (d) Result of proposedmodel.

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Figure 9: Simulation result 3. (a) Input image (Woman). (b) Resultof 3×3 template . (c) Result of 5×5template. (d) Result of proposedmodel.

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