• 한국의류학회지
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• 제41권5호
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• pp.825-839
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• 2017

Generative Art (also called as the art of the algorithm) creates unexpected results, moving autonomously according to rules or algorithms. The evolution of digital media in art, which tries to seek novelty, increases the possibility of new artistic fields; subsequently, this study establishes the basis for new design approaches by analyzing visual cases of Generative Art that have emerged since the 20th century and characteristics expressed on fashion. For the methodology, the study analyzes fashion designs that have emerged since 2000, based on theoretical research that includes literature and research papers relating to Generative Art. According to the study, expression characteristics shown in fashion, based on visual properties of Generative Art, are as follows. First, abstract randomness is expressed with unexpected coincidental forms using movements of a creator and properties of materials as variables in accordance to rules or algorithms. Second, endlessly repeated pattern imitation expresses an emergent shape by endless repetition created by a modular system using rules or 3D printing using a computer algorithm. Third, the systematic variability expresses constantly changing images with a combination of system and digital media by a wearing method. It is expected that design by algorithm becomes a significant method in producing other creative ideas and expressions in modern fashion.

• International journal of advanced smart convergence
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• 제13권3호
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• pp.170-175
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• 2024

The field of Generative AI(Artificial Intelligence) involves a technology that autonomously comprehends user intentions through commands and learns from provided data to generate new content, such as images or text. This capability, which allows autonomous creativity even with design keywords, is anticipated to play a significant role in the domain of visual communication design. This article delves into the tools of generative AI applicable to visual design and the methodology for design creation using these tools. Furthermore, it discusses how designers can interact visually with AI technology in the era of generative AI.

• Journal of Information Processing Systems
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• 제17권1호
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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.

• Current Optics and Photonics
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• 제7권6호
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• pp.655-664
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• 2023

Ghost imaging (GI) technology is developing rapidly, but there are inevitably some limitations such as the influence of atmospheric turbulence. In this paper, we study a ghost imaging system in atmospheric turbulence and use a gamma-gamma (GG) model to simulate the medium to strong range of turbulence distribution. With a compressed sensing (CS) algorithm and generative adversarial network (GAN), the image can be restored well. We analyze the performance of correlation imaging, the influence of atmospheric turbulence and the restoration algorithm's effects. The restored image's peak signal-to-noise ratio (PSNR) and structural similarity index map (SSIM) increased to 21.9 dB and 0.67 dB, respectively. This proves that deep learning (DL) methods can restore a distorted image well, and it has specific significance for computational imaging in noisy and fuzzy environments.

• 문화기술의 융합
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• 제9권4호
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• pp.667-679
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• 2023

이미지 생성형 AI의 등장으로 미술, 디자인 전문가가 아니어도 텍스트 입력을 통해 완성도 높은 그림 작품을 만들 수 있게 되었다. 생성 이미지의 활용 가능성과 예술 산업에 미치는 영향력이 높아짐에 따라 사용자가 AI와 공동 창작하는 과정을 어떻게 인식하는지에 대해 연구 필요성이 제기되고 있다. 이에 본 연구에서는 일반 사용자들을 대상으로 이미지 생성형 AI 창작에 대한 예상 과정과 체감 과정을 알아보고 어떤 과정이 사용자의 창작 주체감에 영향을 미치는지 알아보는 실험 연구를 진행하였다. 연구 결과 사용자들이 기대한 창작 과정과 체감한 창작 과정 간 격차가 있는 것으로 나타났으며 창작 주체감은 낮게 인식하는 경향을 보였다. 이에 AI가 사용자의 창작 의도를 지원하는 조력자의 역할로 작용하여 사용자가 높은 창작 주체감을 경험할 수 있도록 8가지 방법을 제언한다. 본 연구를 통해 사용자 중심적인 창작 경험을 고려하여 향후 이미지 생성형 AI의 발전에 기여할 수 있다.

• 한국게임학회 논문지
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• 제16권5호
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• pp.131-148
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• 2016

게임의 문화적 위상에 관한 논의는 언제나 다양한 예술 영역들과 비교되고 있으며, 게임의 등장 이후부터 현재까지 지속하고 있다. 같은 기간 동안 게임의 문화적 영향력은 점차 증가한 것에 비해, 대중적 인식은 게임의 예술적 가치를 상당히 저평가하고 있다. 그러나 지속해서 게임이 문화적 영향력을 유지하고 우리 사회에 긍정적인 영향력을 제공하기 위해서는 이러한 평가의 변화가 필요하다. 따라서 게임의 관점에서 자신만의 미적 가치를 증명하는 연구가 요구되며, 이에 대한 사회적 동의를 끌어내야 한다. 본 논문은 게임과 예술의 관계와 다양한 유사성에 관한 논의를 통해 게임의 새로운 미학적 잠재성을 탐구한다. 게임아트와 아트게임의 현황 분석을 통해 예술적 관점에서 게임에 대한 견해를 살펴본다. 그리고 이 견해와 견주어 게임적 관점에서 게임의 미학적 대상과 그 가치를 알아본다. 마지막으로 정보미학과 생성미학의 특성을 기반으로 게임미학에 대한 이해를 돕는다.

• Journal of information and communication convergence engineering
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• 제17권1호
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• pp.14-20
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• 2019

Currently, microarray gene expression data take advantage of the sufficient classification of cancers, which addresses the problems relating to cancer causes and treatment regimens. However, the sample size of gene expression data is often restricted, because the price of microarray technology on studies in humans is high. We propose enhancing the gene expression classification of support vector machines with generative adversarial networks (GAN-SVMs). A GAN that generates new data from original training datasets was implemented. The GAN was used in conjunction with nonlinear SVMs that efficiently classify gene expression data. Numerical test results on 20 low-sample-size and very high-dimensional microarray gene expression datasets from the Kent Ridge Biomedical and Array Expression repositories indicate that the model is more accurate than state-of-the-art classifying models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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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.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.343-347
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• 2022

To date, the use of artificial intelligence has already brought certain results in such areas of art as poetry, painting, and music. The development of AI and its application in the creative process opens up new perspectives, expanding the capabilities of authors and attracting a new audience. The purpose of the article is to analyze the essential, artistic, and technological limitations of AI art. The article discusses the methods of attracting AI to artistic practices, carried out a comparative analysis of the methods of using AI in visual art and in the process of writing music, identified typical features in the creative interaction of the author of a work of art with AI. The basic principles of working with AI have been determined based on the analysis of ways of using AI in visual art and music. The importance of neurobiology mechanisms in the course of working with AI has been determined. The authors conclude that art remains an area in which AI still cannot replace humans, but AI contributes to the further formation of methods for modifying and rethinking the data obtained into innovative art projects.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.23-30
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• 2022

Glaucoma is a chronic neuropathy that affects the optic nerve which can lead to blindness. The detection and prediction of glaucoma become possible using deep neural networks. However, the detection performance relies on the availability of a large number of data. Therefore, we propose different frameworks, including a hybrid of a generative adversarial network and a convolutional neural network to automate and increase the performance of glaucoma detection. The proposed frameworks are evaluated using five public glaucoma datasets. The framework which uses a Deconvolutional Generative Adversarial Network (DCGAN) and a DenseNet pre-trained model achieves 99.6%, 99.08%, 99.4%, 98.69%, and 92.95% of classification accuracy on RIMONE, Drishti-GS, ACRIMA, ORIGA-light, and HRF datasets respectively. Based on the experimental results and evaluation, the proposed framework closely competes with the state-of-the-art methods using the five public glaucoma datasets without requiring any manually preprocessing step.