令和3年度 電気・電子・情報関係学会 四国支部連合大会 講演論文集
2021 Shikoku-Section Joint Convention Record of the Institutes of Electrical and Related Engineers

1-8
Application of Periodic Pattern Formation for Number Measurement
and Object Extractionin in Cellular Neural Networks
Sosuke Nakano, Yoko Uwate and Yoshifumi Nishio(Tokushima University)
1. Introduction
In this study, we propose a new method using periodic
pattern formation in the CNN. First, we investigate spot
templates producing periodic patterns. Next, we develop
two image processing algorithms using the template. Oneis an algorithm to measure the number of objects in animage. The other one is an algorithm to extract the objects
in an image.

(a)

(b)

2. Proposed Method
We use SPOT template for the two proposed algorithms.
SPOT template makes white spots in a black area having
spatial uniformity and black spots in a white area having
spatial uniformity in Fig. 1(b). The spots are made only
in areas wider than a certain size. Also, this template enhances periodicity in the area having spatial periodicity. InFig. 1(b), as the result of spatial periodicity enhancement,lines are formed.

(c)(d)
Figure 2: Simulation results. (a) Input image. (b) Binarized
input image. (c) Simulation result of using the SPOT template.
(d) Simulation result of using the proposed method.

(a)
(a)

(b)

(b)

(c)

Figure 1: Simulation results. (a) Input image. (b) Simulation
result of using the SPOT template. (c) Binarized input image.
The background and objects are not separated inFig. 1(c). Thus, the accuracy of number measurement andobject extraction is not good. We can separate the background and objects into spots and lines by using the SPOT
template. Comparing Fig. 1(b) and Fig. 1(c), we can seethat the background and objects are more separated inFig. 1(b) than in Fig. 1(c). As a result, the accuracy ofnumber measurement and object extraction has been improved.

(c)(d)
Figure 3: Simulation results. (a) Input image. (b) Binarized
input image. (c) Simulation result of using the SPOT template.
(d) Simulation result of using the proposed method.

Figure 2(a) shows input image contains 82 objects to bemeasured. In Fig. 2(b), the background is still visible, butin Fig. 2(c), the lines are also displayed in the backgroundarea. This allows us to focus on the object. The number ofobjects to be measured from the image in Fig. 2(d) was 60.
Figure 3(a) shows input image containing the object to beextracted. The background is still visible In Fig. 3(b). Incontrast, the lines are also displayed in the background areain Fig. 3(c). This allows us to focus on the object. Figure3(d) shows only the objects can be extracted.

3. Simulation Results
In this section, we show simulation results for the two algorithms, object measurement and object extraction. TheSPOT template used in both algorithms is described as follows.
SPOT template :
−0.25 −1−1.5−1 −0.25
 −1
2.5
7
2.5−1 A = 7−23.25
7−1.5  −1.5, −1
2.5
7
2.5−1 −0.25 −1−1.5−1 −0.25


B=0
 0

 0
 00000000010000000

4. Conclusions
In this study, we proposed the two new algorisms by using
spot template in CNN. We were able to separate the object
from the background in the image by using SPOT template.
As a result, we were able to improve the accuracy of thenumber measurement and also the extraction of objects.

00 0 ,0 0

I = 0.

(1)–8–