• Journal of Information Technology Services
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• v.22 no.5
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• pp.87-98
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• 2023

Traditionally, tooth restoration has been carried out by replicating teeth using plaster-based materials. However, recent technological advances have simplified the production process through the introduction of computer-aided design(CAD) systems. Nevertheless, dental restoration varies among individuals, and the skill level of dental technicians significantly influences the accuracy of the manufacturing process. To address this challenge, this paper proposes an approach to designing personalized tooth restorations using Generative Adversarial Network(GAN), a widely adopted technique in computer vision. The primary objective of this model is to create customized dental prosthesis for each patient by utilizing 3D data of the specific teeth to be treated and their corresponding opposite tooth. To achieve this, the 3D dental data is converted into a depth map format and used as input data for the GAN model. The proposed model leverages the network architecture of Pixel2Style2Pixel, which has demonstrated superior performance compared to existing models for image conversion and dental prosthesis generation. Furthermore, this approach holds promising potential for future advancements in dental and implant production.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.475-478
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• 2019

Although the work of neural style transfer has shown successful applications in transferring the style of a certain type of artistic painting, it is less effective in transferring Oriental paintings. In this paper, we explore three methods which are effective in transferring Oriental paintings. Then, we take a typical network from each method to carry on the experiment, in view of three different methods to Oriental paintings style transfer effect has carried on the discussion.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.663-672
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• 2023

Most vehicle detection methods have poor vehicle feature extraction performance at night, and their robustness is reduced; hence, this study proposes a night vehicle detection method based on style transfer image enhancement. First, a style transfer model is constructed using cycle generative adversarial networks (cycleGANs). The daytime data in the BDD100K dataset were converted into nighttime data to form a style dataset. The dataset was then divided using its labels. Finally, based on a YOLOv5s network, a nighttime vehicle image is detected for the reliable recognition of vehicle information in a complex environment. The experimental results of the proposed method based on the BDD100K dataset show that the transferred night vehicle images are clear and meet the requirements. The precision, recall, mAP@.5, and mAP@.5:.95 reached 0.696, 0.292, 0.761, and 0.454, respectively.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.727-730
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• 2021

이미지와 비디오 합성 기술에 대한 수요가 늘어남에 따라, 인간의 손에만 의존하여 이미지나 비디오를 합성하는데에는 시간과 자원이 한정적이며, 전문적인 지식을 요한다. 이러한 문제를 해결하기 위해 최근에는 스타일 변환 네트워크를 통해 이미지를 변환하고, 믹싱하여 생성하는 알고리즘이 등장하고 있다. 이에 본 논문에서는 GAN을 이용한 스타일 변환 네트워크를 통한 자연스러운 스타일 믹싱에 대해 연구했다. 먼저 애니메이션 토이 스토리의 등장인물에 대한 데이터를 구축하고, 모델을 학습하고 두 개의 모델을 블렌딩하는 일련의 과정을 거쳐 모델을 준비한다. 그 다음에 블렌딩된 모델을 통해 타겟 이미지에 대하여 스타일 믹싱을 진행하며, 이 때 이미지 해상도와 projection 반복 값으로 스타일 변환 정도를 조절한다. 최종적으로 스타일 믹싱한 결과 이미지들을 바탕으로 하여 스타일 변형, 스타일 합성이 된 인물에 대한 동영상을 생성한다.

• Ocean Systems Engineering
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• v.10 no.4
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• pp.435-449
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• 2020

This paper proposes a method to estimate the underwater target object's yaw angle using a sonar image. A simulator modeling imaging mechanism of a sonar sensor and a generative adversarial network for style transfer generates realistic template images of the target object by predicting shapes according to the viewing angles. Then, the target object's yaw angle can be estimated by comparing the template images and a shape taken in real sonar images. We verified the proposed method by conducting water tank experiments. The proposed method was also applied to AUV in field experiments. The proposed method, which provides bearing information between underwater objects and the sonar sensor, can be applied to algorithms such as underwater localization or multi-view-based underwater object recognition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2115-2127
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• 2021

Although significant progress has been made in synthesizing visually realistic face images by Generative Adversarial Networks (GANs), there still lacks effective approaches to provide fine-grained control over the generation process for semantic facial attribute editing. In this work, we propose a novel cross channel self-attention based generative adversarial network (CCA-GAN), which weights the importance of multiple channels of features and archives pixel-level feature alignment and conversion, to reduce the impact on irrelevant attributes while editing the target attributes. Evaluation results show that CCA-GAN outperforms state-of-the-art models on the CelebA dataset, reducing Fréchet Inception Distance (FID) and Kernel Inception Distance (KID) by 15~28% and 25~100%, respectively. Furthermore, visualization of generated samples confirms the effect of disentanglement of the proposed model.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.1-13
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• 2021

In this research, we study the problem of font image skeletonization using an end-to-end deep adversarial network, in contrast with the state-of-the-art methods that use mathematical algorithms. Several studies have been concerned with skeletonization, but a few have utilized deep learning. Further, no study has considered generative models based on deep neural networks for font character skeletonization, which are more delicate than natural objects. In this work, we take a step closer to producing realistic synthesized skeletons of font characters. We consider using an end-to-end deep adversarial network, SkelGAN, for font-image skeletonization, in contrast with the state-of-the-art methods that use mathematical algorithms. The proposed skeleton generator is proved superior to all well-known mathematical skeletonization methods in terms of character structure, including delicate strokes, serifs, and even special styles. Experimental results also demonstrate the dominance of our method against the state-of-the-art supervised image-to-image translation method in font character skeletonization task.

• The Journal of Korean Medical History
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• v.26 no.1
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• pp.87-96
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• 2013

This study confirmed that a doctor named Lee, Seok-gan whose name has been widely known but whose real identity has remained unclear, was an active Confucian doctor in the 16th century. In addition, through the newly discovered "Daeyakbu" among his family line, writings, and relics that have been handed down in a family, this study looked into his medical philosophy and medicine culture. The author of "Ieseokgangyeongheombang"(Medical Book by Lee, Seok-gan(李石澗), Seok-gan is the same person as an active famous doctor Lee, Seok-gan(李碩幹, 1509-1574) in the 16th century. Such a fact can be confirmed through "Samuiilheombang", "Sauigyeongheombang" and the newly opened "Ieseokgangyeongheombang". Lee, Seok-gan was born in the 4th ruling year of king Jungjong (1509) and was active as a doctor until the 7th ruling year of king Seonjo(1547); his first name is Jungim with the pen name-Chodang, and he used a doctor name of 'Seokgan.' He was known as a divine doctor, and there have been left lots of anecdotes in relation with Lee, Seok-gan. Legend has it that Seokgan went to China to give treatment to the empress, and a heavenly peach pattern drinking cup and a house, which the emperor bestowed on Seokgan in return for his great services, still have remained up to the present. Usually, Seokgan interacted with Toegye Lee Hwang and his literary persons, and with his excellent medical skills, Seokgan once gave treatment to Toegye at the time of his death free of charge. His medical skills have been handed down in his family, and his descendant Lee, Ui-tae(around 1700) compiled a medical book titled "Gyeongheombangwhipyeon(經驗方彙編)". Out of Lee, Seok-gan's keepsakes which were donated to Sosu museums by his descendant family, 4 sorts of 'Gwabu'(writings of fruit trees) including "Daeyakbu" were discovered. It's rare to find a literary work left by a medical figure like this, so these discoveries have a deep meaning even from a medicine culture level. Particularly, "Daeyakbu" includes the typical "Uigukron". The "Uigukron", which develops its story by contrasting politics with medicine, has a unique writing style as one of the representative explanatory methods of scholars' position during the Joseon Dynasty; in addition, the distinctive feature of "Uigukron" is that it was created in the form of 'Gabu' other than a prose.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.35-43
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• 2022

Makeup is the most common way to improve a person's appearance. However, since makeup styles are very diverse, there are many time and cost problems for an individual to apply makeup directly to himself/herself.. Accordingly, the need for makeup automation is increasing. Makeup transfer is being studied for makeup automation. Makeup transfer is a field of applying makeup style to a face image without makeup. Makeup transfer can be divided into a traditional image processing-based method and a deep learning-based method. In particular, in deep learning-based methods, many studies based on Generative Adversarial Networks have been performed. However, both methods have disadvantages in that the resulting image is unnatural, the result of makeup conversion is not clear, and it is smeared or heavily influenced by the makeup style face image. In order to express the clear boundary of makeup and to alleviate the influence of makeup style facial images, this study divides the makeup area and calculates the loss function using HoG (Histogram of Gradient). HoG is a method of extracting image features through the size and directionality of edges present in the image. Through this, we propose a makeup transfer network that performs robust learning on edges.By comparing the image generated through the proposed model with the image generated through BeautyGAN used as the base model, it was confirmed that the performance of the model proposed in this study was superior, and the method of using facial information that can be additionally presented as a future study.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.2
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• pp.201-210
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• 2023

Deep learning-based face verificattion model show high performance and are used in many fields, but there is a possibility the user's face image may be leaked in the process of inputting the face image to the model. Althoughde-identification technology exists as a method for minimizing the exposure of face features, there is a problemin that verification performance decreases when the existing technology is applied. In this paper, after combining the face features of other person, a de-identified face image is created through StyleGAN. In addition, we propose a method of optimizingthe combining ratio of features according to the face verification model using HopSkipJumpAttack. We visualize the images generated by the proposed method to check the de-identification performance, and evaluate the ability to maintain the performance of the face verification model through experiments. That is, face verification can be performed using the de-identified image generated through the proposed method, and leakage of face personal information can be prevented.