• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.171-187
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• 2023

When a person's face is recognized through a recording device such as a low-pixel surveillance camera, it is difficult to capture the face due to low image quality. In situations where it is difficult to recognize a person's face, problems such as not being able to identify a criminal suspect or a missing person may occur. Existing studies on face recognition used refined datasets, so the performance could not be measured in various environments. Therefore, to solve the problem of poor face recognition performance in low-quality images, this paper proposes a method to generate high-quality images by performing image quality improvement on low-quality facial images considering various environments, and then improve the performance of facial feature point detection. To confirm the practical applicability of the proposed architecture, an experiment was conducted by selecting a data set in which people appear relatively small in the entire image. In addition, by choosing a facial image dataset considering the mask-wearing situation, the possibility of expanding to real problems was explored. As a result of measuring the performance of the feature point detection model by improving the image quality of the face image, it was confirmed that the face detection after improvement was enhanced by an average of 3.47 times in the case of images without a mask and 9.92 times in the case of wearing a mask. It was confirmed that the RMSE for facial feature points decreased by an average of 8.49 times when wearing a mask and by an average of 2.02 times when not wearing a mask. Therefore, it was possible to verify the applicability of the proposed method by increasing the recognition rate for facial images captured in low quality through image quality improvement.

• Journal of dental hygiene science
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• v.15 no.6
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• pp.689-695
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• 2015

The aim of this study was to examine the image for dental hygienists and career consideration in academic high school students. A self-reported questionnaire was surveyed by 199 high school students in Gwangju. The questionnaire composed subject's general characteristics, image for dental hygienists, career consideration. The data were analyzed for independent t-test or Mann-Whitney test, one-way ANOVA or Kruskal wallis test and pearson's correlation coefficient by using IBM SPSS Statistics ver. 21.0 program. The average of total image was 3.36, personal images were 3.64, occupational images were 3.47, business images were 3.42, social images were 2.83 in the five-point scale. The image of dental hygienists was higher significantly in case of having a dental practitioner in the family, treatment experience less than 1 year, and treatment in dental hospital. The participants who considered to be dental hygienist were higher significantly in occupational and social images. There were positive correlations among the personal, occupational, business, social images of dental hygienists. These results showed that general images for dental hygienists were slightly positive but among them, social image was the lowest level. It showed that most students didn't consider to be dental hygienist. Therefore, we suggested to enhance positive recognition for dental hygienists be needed.

• The Journal of the Korea Contents Association
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• v.18 no.12
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• pp.118-129
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• 2018

In AI chatbot service via a messenger, a profile image of the chatbot is the first thing that users see to communicate with the chatbot. This profile image not only manages an impression about the profile owner in SNS on followers, but also makes an important impression about chatbot services on users. Thus motivated, this study investigates proper profile images tailored for the types of chatbot services and users. Specifically, I reviewed the preferred images and expressions of chatbots for each purpose of chatbot service. Then, in a case study, I collected and analyzed the representative chatbot profile images for the purpose of fun and counseling. The profile images are categorized as robot, human, animal, and abstract images. Based on these categories, I surveyed the preferred profile image of the chatbot service in either the text type or image type alternatives. For the purpose of fun, in the text version, I found that both men and women preferred a human image to others. However, in the image version, men preferred woman and robot images while women preferred cute animation character and robot images. For counseling services, both men and women preferred woman and animal images most, which is similar to the results of the text version of questionnaires as well. While both genders consistently preferred real photo images, women tend to like abstract images more than men do. I expect that the results of this study would be useful to develop the proper profile images of AI chatbot for each service purpose.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.59-60
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• 2022

본 논문에서는 유아 영어 단어 학습 애플리케이션을 설계하고 구현한다. 이 애플리케이션은 키넥트 센서의 음성 인식 기능을 활용하여 동물과 음식 분야의 단어 학습 기능을 제공한다. 화면에 출력된 이미지에 해당하는 영어 단어를 말하면 키넥트 센서에서 그 음성을 인식하여 해당 단어의 발음이 정확한지 판별한다. 주어진 시간 내에 다양한 단어를 정확하게 발음함으로써 높은 점수를 취득하도록 구현한다.

• Archives of design research
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• v.17 no.1
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• pp.5-14
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• 2004

The study addressed a direction that environmental facilities along avenues will make a progress via urban environment as well as location marketing. The space is specified the campus avenue, which is located in Jongro-gu at Seoul. With the emergence of digital information era, both people' life style and behaviors have been changing in many different ways. As the first approach to the direction, the historical study of the avenue is examined by focusing on the change. All possible related status such as location marketing strategy and various spatial characteristics, environment of the avenue and its facilities are investigated and users' characteristics are analyzed. Through these processes, a new direction and role of design are proposed and are believed to improve the avenue' image and to rediscover its own identity and to make the avenue alive as it used to be. The new proposed direction showed how to approach to design as well as to system of environmental facilities along avenues and in addition to highlighting continuous and synthetic plan. Furthermore, It is expected that both the efforts to improve the avenue' image based on its own cultural characteristic and to foster the cultural-commercialized facilities will contribute to the local residents' life standard and its economic status.

• Journal of the Korea Computer Graphics Society
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• v.29 no.3
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• pp.79-84
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• 2023

Image generation models have been applied in various fields to overcome data sparsity, time and cost issues. However, it has limitations in generating images from regular pattern images and detecting defects in such data. In this paper, we verified the feasibility of the image generation model to generate pattern images and applied it to data augmentation for defect detection of OLED panels. The data required to train an OLED defect detection model is difficult to obtain due to the high cost of OLED panels. Therefore, even if the data set is obtained, it is necessary to define and classify various defect types. This paper introduces an OLED panel defect data acquisition system that acquires a hypothetical data set and augments the data with an image generation model. In addition, the difficulty of generating pattern images in the diffusion model is identified and a possibility is proposed, and the limitations of data augmentation and defect detection data augmentation using the image generation model are improved.

• Journal of Practical Agriculture & Fisheries Research
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• v.21 no.2
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• pp.103-114
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• 2019

In this study we examined the preferences of college life factors for students at Korea National College of Agriculture and Fisheries(KNCAF). Analytical techniques of unstructured data used opinion mining and text mining techniques, and the results of text mining were visualized as word cloud. And those results were used for statistical analysis of the students' willingness to farm after graduation. The items of the favorable survey consisted of 10 items in 5 areas including university image, self-capacity, dormitory, education system, and future vision. After classifying the emotions of positive and negative in the collected questionnaire, a dictionary of positive and negative was created to evaluate the preference. The items of 'college image' at the time of university support, 'self after 10 years' after graduation, 'self-capacity' and 'present KNCAF' showed high positive emotion. On the other hand, positive emotion was low in the items of 'college dormitory', 'educational course', 'long-term field practice' and 'future of Korean agriculture'. In the cross-analysis of the difference in the will to farming according to gender, farming base, and entrance motivation, the will to farm according to gender and entrance motivation showed statistically significant results, but it was not significant in farming base. Also in binary logistic regression analysis on the will to farming, the statistically significant variable was found to be 'motivation for admission'

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1407-1422
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• 2022

This study was conducted to classify tree species and assess the classification accuracy, using SE-Inception, a classification-based deep learning model. The input images of the dataset used Worldview-3 and GeoEye-1 images, and the size of the input images was divided into 10 × 10 m, 30 × 30 m, and 50 × 50 m to compare and evaluate the accuracy of classification of tree species. The label data was divided into five tree species (Pinus densiflora, Pinus koraiensis, Larix kaempferi, Abies holophylla Maxim. and Quercus) by visually interpreting the divided image, and then labeling was performed manually. The dataset constructed a total of 2,429 images, of which about 85% was used as learning data and about 15% as verification data. As a result of classification using the deep learning model, the overall accuracy of up to 78% was achieved when using the Worldview-3 image, the accuracy of up to 84% when using the GeoEye-1 image, and the classification accuracy was high performance. In particular, Quercus showed high accuracy of more than 85% in F₁ regardless of the input image size, but trees with similar spectral characteristics such as Pinus densiflora and Pinus koraiensis had many errors. Therefore, there may be limitations in extracting feature amount only with spectral information of satellite images, and classification accuracy may be improved by using images containing various pattern information such as vegetation index and Gray-Level Co-occurrence Matrix (GLCM).

• Journal of The Korean Association For Science Education
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• v.43 no.4
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• pp.333-349
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• 2023

This study employs Lacan's psychoanalytic approach to reinterpret the images of others and teachers that influence the process of self-formation within the teaching profession as students enter a university of education Seventy-four first- and second-year students majoring in science education at the College of Education from three regions across the country participated in this study, which was conducted using Lacan's L schematic as a representative theoretical framework. Through qualitative analysis and a word cloud analysis, it was confirmed that the students developed perceptions of the teaching profession based on somewhat fictitious and unrealistic teacher images, while others actively intervened in the process of career decision-making. In addition, although parents or teachers mainly occupied the realm of the Other, it was found that they failed to appropriately fulfill the role of the Other, in that they should have corrected the fictional image of teachers. Accordingly, it is necessary to recognize the limitations of ego-psychological career education that can deepen fixations on distorted self-images and, therefore, seek a new career education and counseling model through a psychoanalytic approach.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.525-528
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• 2012

최근 무선 인터넷 서비스의 활성화와 함께 모바일 환경에서 브라우저의 사용이 급증하고 있으며, 모바일 사용자들은 PC 수준의 품질을 요구하고 있다. 이에 따라 모바일 웹브라우저도 WAP 방식이 아닌 풀브라우징(full browsing) 방식으로 전환되고 있다. 하지만, 모바일 단말 환경에서 웹브라우저의 성능에는 많은 제약사항이 존재하며, 부족한 메모리, 저 사양의 CPU, 낮은 네트워크 속도, 그리고 브라우저의 엔진 문제 등으로 인해 여전히 사용자들의 브라우저 로딩에 대한 체감 속도는 낮은 편이다. 본 논문에서는 저사양 휴대 단말 환경에서의 브라우저 로딩 속도를 개선할 수 있는 방안을 제시한다. 제안 방식에서는 텍스트와 이미지 등 데이터 타입을 분류하여 부하가 적게 걸리는 텍스트 레이아웃을 먼저 보여줌으로써 사용자의 체감속도를 향상시키고, 아울러 이미지가 커서 렌더링(rendering) 시간이 오래 걸리는 경우 이미지를 축소하거나 화질을 낮추는 방식으로 렌더링 부하를 줄여서 페이지 로딩 시간을 단축시키는 방법을 사용한다. 실험 결과, 제안 기법을 사용하는 경우 현재 사용하는 방법에 비해 이미지가 적은 Web 페이지의 경우 1st drawing 77.04%, full drawing 5.47%, 이미지가 많은 페이지의 경우 26.32%의 로딩 시간을 단축시킬 수 있음을 확인하였다.