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      계산 이미지의 탄생 - 장치에서 알고리즘으로 - = The birth of computational image - from apparatus to algorithm -

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      https://www.riss.kr/link?id=A107342694

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      다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

      This article deals with the changes triggered by artificial intelligence related to image production. The image originally started as a “manual image” drawn by human hands and then differentiated into a “technical image” produced by apparatus in the 19th century. Since then, with the development of image generation algorithms, the automaticity of image production has become more advanced. This generative model, represented by GAN, produces images only by 'calculation' based on probability and statistics. Currently, GAN is receiving the most attention in the field of computer vision, and it excels in various tasks such as creating high-resolution images, translating images, and compositing photos. GAN learns the original photos and creates an image similar to it, which goes beyond the limitations of apparatus such as human hands and cameras. This 'computational image' belongs to the category of 'technical image' in a broad sense, but differs from photography, the first technical image in that there is no indication object. In addition, the automaticity of the program, the nature of the apparatus, is accelerating the exclusion of humans from the image production process in an extreme form. As a result, humans cannot reflect their intentions in image production and are in a position to become a simple consumer of images.
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      This article deals with the changes triggered by artificial intelligence related to image production. The image originally started as a “manual image” drawn by human hands and then differentiated into a “technical image” produced by apparatus ...

      This article deals with the changes triggered by artificial intelligence related to image production. The image originally started as a “manual image” drawn by human hands and then differentiated into a “technical image” produced by apparatus in the 19th century. Since then, with the development of image generation algorithms, the automaticity of image production has become more advanced. This generative model, represented by GAN, produces images only by 'calculation' based on probability and statistics. Currently, GAN is receiving the most attention in the field of computer vision, and it excels in various tasks such as creating high-resolution images, translating images, and compositing photos. GAN learns the original photos and creates an image similar to it, which goes beyond the limitations of apparatus such as human hands and cameras. This 'computational image' belongs to the category of 'technical image' in a broad sense, but differs from photography, the first technical image in that there is no indication object. In addition, the automaticity of the program, the nature of the apparatus, is accelerating the exclusion of humans from the image production process in an extreme form. As a result, humans cannot reflect their intentions in image production and are in a position to become a simple consumer of images.

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      참고문헌 (Reference)

      1 Flusser, Vilem, "Vilem(1985). Into the universe of technical image" University of Minnesota Press 2011

      2 Radford, Alec, "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks"

      3 Flusser, Vilem, "Towards a philosophy of photography" Reaktion Books Ltd 1983

      4 Jetchev, Nikolay, "Texture Synthesis with Spatial Generative Adversarial Networks"

      5 Choi, Yunjey, "StarGAN:Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation"

      6 Zhang, Han, "Self-Attention Generative Adversarial Networks" 2019

      7 Pan, Zhaoqing, "Recent Progress on Generative Adversarial Networks (GANs): a survey" 7 : 36322-36333, 2019

      8 Karras, Tero, "Progressive Growing of GANs for improved quality, stability, and variation"

      9 Li, Chuan, "Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks"

      10 Ledig, Christian, "Photo-Ralistic single image Super-Resolution using a Generative Adversarial Network"

      1 Flusser, Vilem, "Vilem(1985). Into the universe of technical image" University of Minnesota Press 2011

      2 Radford, Alec, "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks"

      3 Flusser, Vilem, "Towards a philosophy of photography" Reaktion Books Ltd 1983

      4 Jetchev, Nikolay, "Texture Synthesis with Spatial Generative Adversarial Networks"

      5 Choi, Yunjey, "StarGAN:Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation"

      6 Zhang, Han, "Self-Attention Generative Adversarial Networks" 2019

      7 Pan, Zhaoqing, "Recent Progress on Generative Adversarial Networks (GANs): a survey" 7 : 36322-36333, 2019

      8 Karras, Tero, "Progressive Growing of GANs for improved quality, stability, and variation"

      9 Li, Chuan, "Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks"

      10 Ledig, Christian, "Photo-Ralistic single image Super-Resolution using a Generative Adversarial Network"

      11 Goodfellow, Ian, "NIPS 2016Tutorial: Generative Adversarial Networks"

      12 Kim, Taeksoo, "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"

      13 Bergmann, Urs, "Learning Texture Manifolds with the Periodic Spatial GAN"

      14 Brock, Andrew, "Large Scale GAN Training for Hige Fidelity Natural Image Synthesis"

      15 Salimans, Tim, "Improved Techniques for Training GANs"

      16 Isola, Phillip, "Image-to-Image Translation with Conditional Adversarial Networks"

      17 Goodfellow, Ian, "Generative Adversarial Nets"

      18 Pyungjong Park, "From program to posgram : The Fundamental Problems of Photography and Artificial Intelligence Art" Society of Contemporary Art Science 22 (22): 107-130, 2018

      19 Mirza, Mehdi, "Conditional Generative Adversarial Nets"

      20 Indolia, Sakshi, "Conceptual Understanding of Convolutional Neural Network – A Deep Learning Approach" 2018 : 679-688, 2018

      21 Huang, Rui, "Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis" 2439-2448, 2017

      22 O’Shea, Keiron, "An Introduction to Convolutional Neural Networks"

      23 Besan‡on, "Alain. L’Image interdite: une histoire intellectuelle de l’iconoclasme" Fayard 1994

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2023 평가 재인증평가 신청대상 (재인증)
      2020-01-01 등재 등재학술지 선정 (재인증) KCI등재
      2018-01-01 등재 등재후보학술지 선정 (신규평가) KCI등재후보
      2016-12-01 등재 등재후보 탈락 (계속평가)
      2015-12-01 등재 등재후보로 하락 (기타) KCI등재후보
      2011-01-01 등재 등재학술지 유지 (등재유지) KCI등재
      2009-01-01 등재 등재학술지 유지 (등재유지) KCI등재
      2007-01-01 등재 등재 1차 FAIL (등재유지) KCI등재
      2004-01-01 등재 등재학술지 선정 (등재후보2차) KCI등재
      2003-01-01 등재 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2002-01-01 등재 등재후보 1차 FAIL (등재후보1차) KCI등재후보
      2000-07-01 등재 등재후보학술지 선정 (신규평가) KCI등재후보
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