• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.5
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• pp.1051-1058
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• 2018

This paper propose image processing algorithm to prevent safety accidents near by subway platform screen door(PSD). First, edges of the subway PSD images are detected and the boundary line between PSD and subway platform is detected to decide people's approach to the PSD using Hough transform. To do this, we draw the boundary line between the PSD and platform, to detect the boundary line and to decide the people's approach to the detected line is completely connected or not. Generally, edge is the basic characteristic of image; thus, edge detection is very important in image processing applications and computer vision area. The conventional edge detection methods such as Roberts, Sobel, Prewitt, and Laplacian etc, which are using a fixed value of mask, and morphological gradient from the structuring element of view and Canny edge detector are widely used. In this paper, we propose the detection algorithm about the people's approach to the subway PSD to prevent the safety accidents by using Canny edge detector and Hough transform and the computer simulation shows the results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1176-1182
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• 2021

Recently, with the development of artificial intelligence and IoT technology, automation, and unmanned work are progressing in various fields, and the importance of image processing, which is the basis of AI object recognition, is increasing. In particular, in systems that require detailed data processing, noise removal is used as a preprocessing step, but the existing algorithm does not consider the noise level of the image, so it has the disadvantage of blurring in the filtering process. Therefore, in this paper, we propose a modified Gaussian filter that determines the weight by determining the noise level of the image. The proposed algorithm obtains the noise estimate for the AWGN of the image using quadtree segmentation, determines the Gaussian weight and the pixel weight, and obtains the final output by convolution with the local mask. To evaluate the proposed algorithm, it was simulated compared to the existing method, and superior performance was confirmed compared to the existing method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.526-532
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• 2022

With the development of IoT technology, image processing is being utilized in various fields such as image analysis, image recognition, medical industry, and factory automation. Noise is generated in image data from causes such as defect in transmission line. Image noise must be removed because it damages the performance of the image processing application program. Salt and Pepper noise is a representative type of image noise, and various studies have been conducted to remove Salt and Pepper noise. Widely known methods include A-TMF, AFMF, and SDWF. However, as the noise density increases, the performance deteriorates. Thus, this paper proposes an algorithm that performs filtering using a fuzzy logic weight mask only in case of noise after noise determination. In order to prove the noise removal performance of the proposed algorithm, an experiment was performed on images with 10% to 90% noise added and the PSNR was compared.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.207-213
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• 2022

Recently, with the improvement of the performance of IoT technology and AI, automation and unmanned work are progressing in a wide range of fields, and interest in image processing, which is the basis of automation such as object recognition and object classification, is increasing. Image noise removal is an important process used as a preprocessing step in an image processing system, and various studies have been conducted. However, in most cases, it is difficult to preserve detailed information due to the smoothing effect in high-frequency components such as edges. In this paper, we propose an algorithm to restore damaged images in AWGN(additive white Gaussian noise) using fuzzy weights based on Gaussian distribution. The proposed algorithm switched the filtering process by comparing the filtering mask and the noise estimate with each other, and reconstructed the image by calculating the fuzzy weights according to the low-frequency and high-frequency components of the image.

• Journal of Practical Engineering Education
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• v.14 no.1
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• pp.99-108
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• 2022

As the demand for kiosks increases, more users complain of discomfort. Accordingly, a kiosk that enables easy menu selection and order by producing a voice-based interactive service is produced and provided in the form of a web. It implements voice functions based on the Annyang API and SpeechSynthesis API, and understands the user's intention through Dialogflow. And discuss how to implement this process based on Rest API. In addition, the recommendation system is applied based on collaborative filtering to improve the low consumer accessibility of existing kiosks, and to prevent infection caused by droplets during the use of voice recognition services, it provides the ability to check the wearing of masks before using the service.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.407-409
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• 2021

Under the influence of the 4th industrial revolution, various technologies such as artificial intelligence and automation are being grafted into industrial sites, and accordingly, the importance of data processing is increasing. Digital images may generate noise due to various reasons, and may affect various systems such as image recognition and classification and object tracking. To compensate for these shortcomings, we propose an image restoration algorithm based on pattern information of non-noise pixels. According to the distribution of non-noise pixels inside the filtering mask, the proposed algorithm switched the filtering process by dividing the interpolation method into a pattern that can be applied, a pattern based on region division, and a randomly arranged pixel pattern. preserves and restores the image. The proposed algorithm showed superior performance compared to the existing impulse noise removal algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.250-252
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• 2022

Recently, with the development of IoT technology and AI, unmanned and automated systems are progressing in various fields, and various application technologies are being studied in systems using algorithms such as object detection, recognition, and tracking. In the case of a system operating based on an image, noise removal is performed as a pre-processing process, and precise noise removal is sometimes required depending on the environment of the system. In this paper, we propose a modified central weight filter algorithm using pixel division of local regions to minimize the blurring that tends to occur in the filtering process and to emphasize the details of the resulting image. In the proposed algorithm, when a pixel of a local area is divided into two areas, the center of the dominant area among the divided areas is set as a criterion for the weight filter algorithm. The resulting image is calculated by convolving the transformed center weight with the pixel value inside the filtering mask.

• Journal of the Korean Society of Food Science and Nutrition
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• v.44 no.1
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• pp.145-151
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• 2015

The purpose of this study was to evaluate foodservice hygiene in middle school students by analyzing the importance and performance of school foodservice by meal service area in Busan. Questionnaires were administered to 826 students in 10 middle schools (five schools for classroom service, five schools for dining room service). The average importance and performance scores were 4.11/5.00 and 3.38/5.00 for classroom service and 4.34/5.00 and 3.89/5.00 for dining room service. Dining room service had significantly (P<0.001) higher average scores importance and performance of food hygiene, environment hygiene, equipment hygiene, and employee hygiene than those of classroom service. The gap average score of dining room service was significantly (P<0.001) lower than those of classroom service. In the importance-performance analysis, 'cleanliness of tray' showed high importance and low performance for classroom service and dining room service. Areas of low importance and low performance were 'tray drying', 'cleanliness of employee' clothes', 'wearing sanitary gown, cap', 'wearing sanitary mask', and 'wearing sanitary gloves' for the classroom service, whereas 'tray drying', 'comfortable atmosphere in diningroom', 'well ventilated', 'no odor of the food in classroom', and 'cleanliness around rubbish bins' were relevant for dining room service. These findings suggest that employee hygiene management should be intensively managed for classroom service, and equipment hygiene management must be improved for classroom service and dining room service, especially cleanliness of tray. Meal service places should be changed to the dining room with support the government and private sector.

• Journal of agricultural medicine and community health
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• v.46 no.2
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• pp.78-88
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• 2021

Objectives: This study aimed to investigate health risk awareness pertaining to fine dust exposure and the use of face masks in farmers, as well as their attitude toward education regarding fine dust-related respiratory disease prevention. Methods: In total, 295 farmers were interviewed in a survey using a structured questionnaire to obtain data on general characteristics, farming-related characteristics, health risk awareness pertaining to fine dust exposure, attitude toward education on fine dust-related respiratory disease prevention and the use of face masks. This study was analyzed the correlation between the perceived susceptibility to fine dust exposure and willingness to participate in education on fine dust-related respiratory disease prevention. Results: The mean score for perceived susceptibility to fine dust exposure was 3.8 (out of 5), and the participants were highly willing to receive education on fine dust-related respiratory disease prevention. In Multiple response analysis of reactions to exposure to fine dust generated during work, 221 participants responded that they practiced at least one preventive action; participants gave a positive response to "wearing masks" (56.1%), "personal hygiene, such as hand washing." (52.9%). In terms of education methods, 94 (33.6%) participants preferred to learn online or via text messages. Conclusions: The significant correlation between the perceived susceptibility to fine dust exposure and willingness to participate in education on fine dust-related respiratory disease prevention shows the importance of promoting education on prevention. The results of this study can help understand as reference for education on fine dust-related respiratory disease prevention.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.149-162
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• 2005

This paper suggest a way to detect a specific wanted figure in public places such as subway stations and banks by comparing color face images extracted from the real time CCTV with the face images of designated specific figures. Assuming that the characteristic of the surveillance camera allows the face information in screens to change arbitrarily and to contain information on numerous faces, the accurate detection of the face area was focused. To solve this problem, the normalization work using subsampling with $20{\times}20$ pixels on arbitrary face images, which is based on the Perceptron Neural Network model suggested by R. Rosenblatt, created the effect of recogning the whole face. The optimal linear filter and the histogram shaper technique were employed to minimize the outside interference such as lightings and light. The addition operation of the egg-shaped masks was added to the pre-treatment process to minimize unnecessary work. The images finished with the pre-treatment process were divided into three reception fields and the information on the specific location of eyes, nose, and mouths was determined through the neural network. Furthermore, the precision of results was improved by constructing the three single-set network system with different initial values in a row.