令和2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 (愛媛大学)
2020 SHIKOKU-SECTION JOINT CONVENTION RECORD OF THE INSTITUTES OF ELECTRICAL AND RELATED ENGINEERS (EHIME UNIV.)

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Counting Object in Images by Using Cellular Neural Networks
Sosuke Nakano Kodai Kitamura Yoko Uwate Yoshifumi Nishio(Tokushima University)

1. Introduction
In this study, as a new approch for image processing, we
propose a new algorithm using Cellular Neural Networks(CNN) to count the number of objects in an input image.
This algorithm creates spatially uniform spots in the image
and uses it to count the number of objects.

2. Proposed Method
The proposed algorithm is shown in Fig. 1. The algorithm creates the spots throughout the image, leaving only
the spots in the objects. We count the number of object
by the proportion of that spots. The letter between parenthetic in Fig. 1 correspond to the letter between parentheticin Fig. 2.

Step7: Size Reduction
The size of the remaining spots in step 6 are reductedusing switching between State Value template and Black
Filler template. The step is done to shape the remainingspots.
Step8: Count Object
The image in step 7 are compared with the pitch black
image. The number of objects are counted with differences
above the threshold.

3. Simulation Results
In this section, we show the simulation results of the
propossed algorithm counting objects in image.

(a)

(b)

Figure 1: The proposed algorithm.
Each step of the proposed algorithm is as follows.
Step1: Threshold
The input image is binarized by switching between theBlack Filler template and the White Filler template.
Step2.1: Noise Remove(c)(d)
Figure 2: Simulation results. (a) Input image. (b) Simula-

Noise are removed using the Small Object Remover template for the binarized image in step 1.

tion result of Threshold. (c) Simulation result up to SPOT. (d)Output image.

Step2.2: SPOTFigure 2 (a) shows the image containing objects. Figure 2 (b) shows the binarized image. Figure 2 (c) shows tocreate spots in a black area having spatial uniformity andblack spots in a white area having spatial uniformity only
in areas wider than a certain size. Figure 2 (d) shows theoutput image. The number of objects is 82 in Fig 2 (a).
However, from Fig. 2 (b), the number of binarized objects
is 61. In contrast, from Fig. 2 (d), the number of spot is137. As the results, we confirmed that number of objects
can be known by spots inside the object.

The spot is created using the SPOT template for thebinarized image in step 1.
Step3: Logic Difference
The Logic Difference template is used to subtract theimages in step 2.1 and step 2.2. This step is done to remove
background spots.
Step4: Logic NOT
The image in step 3 is inverted for the rest of the process.
Step5: Deletes Pixels

4. Conclusions

1-pixel or 2-pixel objects in the image in step 4 are removed using switching between State Value template andSmall Object Remover template.

In this study, the accuracy of the proposed algorithm isreduced due to more binarization than necessary. We confirmed that the accuracy of the proposed algorithm varies
with the accuracy of the binarization. Therefore, in the future, it is necessary to improve the accuracy of the proposed
algorithm by enhancing binarization.

Step6: Figure Extractor
Isolated pixels from the step 5 are removed using theDeletes Isolated White Pixels template.