Auswahl der wissenschaftlichen Literatur zum Thema „Automatic Colorization“
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Zeitschriftenartikel zum Thema "Automatic Colorization"
Aoki, Terumasa, und Van Nguyen. „Global Distribution Adjustment and Nonlinear Feature Transformation for Automatic Colorization“. Advances in Multimedia 2018 (2018): 1–15. http://dx.doi.org/10.1155/2018/1504691.
Der volle Inhalt der QuelleAlam khan, Sharique, und Alok Katiyar. „Automatic colorization of natural images using deep learning“. YMER Digital 21, Nr. 05 (20.05.2022): 946–51. http://dx.doi.org/10.37896/ymer21.05/a6.
Der volle Inhalt der QuellePrasanna, N. Lakshmi, Sk Sohal Rehman, V. Naga Phani, S. Koteswara Rao und T. Ram Santosh. „AUTOMATIC COLORIZATION USING CONVOLUTIONAL NEURAL NETWORKS“. International Journal of Computer Science and Mobile Computing 10, Nr. 7 (30.07.2021): 10–19. http://dx.doi.org/10.47760/ijcsmc.2021.v10i07.002.
Der volle Inhalt der QuelleNetha, Guda Pranay, M. S. S. Manohar, M. Sai Amartya Maruth und Ganjikunta Ganesh Kumar. „Colourization of Black and White Images using Deep Learning“. International Journal of Computer Science and Mobile Computing 11, Nr. 1 (30.01.2022): 116–21. http://dx.doi.org/10.47760/ijcsmc.2022.v11i01.014.
Der volle Inhalt der QuelleFarella, Elisa Mariarosaria, Salim Malek und Fabio Remondino. „Colorizing the Past: Deep Learning for the Automatic Colorization of Historical Aerial Images“. Journal of Imaging 8, Nr. 10 (01.10.2022): 269. http://dx.doi.org/10.3390/jimaging8100269.
Der volle Inhalt der QuelleXu, Min, und YouDong Ding. „Fully automatic image colorization based on semantic segmentation technology“. PLOS ONE 16, Nr. 11 (30.11.2021): e0259953. http://dx.doi.org/10.1371/journal.pone.0259953.
Der volle Inhalt der QuelleLiu, Shiguang, und Xiang Zhang. „Automatic grayscale image colorization using histogram regression“. Pattern Recognition Letters 33, Nr. 13 (Oktober 2012): 1673–81. http://dx.doi.org/10.1016/j.patrec.2012.06.001.
Der volle Inhalt der QuelleHuang, Zhitong, Nanxuan Zhao und Jing Liao. „UniColor“. ACM Transactions on Graphics 41, Nr. 6 (30.11.2022): 1–16. http://dx.doi.org/10.1145/3550454.3555471.
Der volle Inhalt der QuelleFurusawa, Chie. „2-1 Colorization Techniques for Manga and Line Drawings; Comicolorization: Semi-Automatic Manga Colorization“. Journal of The Institute of Image Information and Television Engineers 72, Nr. 5 (2018): 347–52. http://dx.doi.org/10.3169/itej.72.347.
Der volle Inhalt der QuelleSugumar, S. J. „Colorization of Digital Images: An Automatic and Efficient Approach through Deep learning“. Journal of Innovative Image Processing 4, Nr. 3 (16.09.2022): 183–94. http://dx.doi.org/10.36548/jiip.2022.3.006.
Der volle Inhalt der QuelleDissertationen zum Thema "Automatic Colorization"
Hati, Yliess. „Expression Créative Assistée par IA : Le Cas de La Colorisation Automatique de Line Art“. Electronic Thesis or Diss., Reims, 2023. http://www.theses.fr/2023REIMS060.
Der volle Inhalt der QuelleAutomatic lineart colorization is a challenging task for Computer Vision. Con- trary to grayscale images, linearts lack semantic information such as shading and texture, making the task even more difficult.This thesis dissertation is built upon related works and explores the use of modern generative Artificial Intelligence (AI) architectures such as Generative Adversarial Networks (GANs) and Denoising Diffusion Models (DDMs) to both improve the quality of previous techniques, as well as better capturing the user colorization intent throughout three contributions: PaintsTorch, StencilTorch and StablePaint.As a result, an iterative and interactive framework based on colored strokes and masks provided by the end user is built to foster Human-Machine collaboration in favour of natural, and emerging workflows inspired by digital painting processes
Chang, Yu-wei, und 張佑瑋. „Automatic grayscale image colorization“. Thesis, 2005. http://ndltd.ncl.edu.tw/handle/89981338295360370277.
Der volle Inhalt der QuelleChen, Yung-An, und 陳勇安. „Automatic Colorization Defects Inspection using Deep Learning Network“. Thesis, 2019. http://ndltd.ncl.edu.tw/handle/3zbtg6.
Der volle Inhalt der QuelleBuchteile zum Thema "Automatic Colorization"
Tran, Tan-Bao, und Thai-Son Tran. „Automatic Natural Image Colorization“. In Intelligent Information and Database Systems, 612–21. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41964-6_53.
Der volle Inhalt der QuelleLarsson, Gustav, Michael Maire und Gregory Shakhnarovich. „Learning Representations for Automatic Colorization“. In Computer Vision – ECCV 2016, 577–93. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46493-0_35.
Der volle Inhalt der QuelleDhir, Rashi, Meghna Ashok, Shilpa Gite und Ketan Kotecha. „Automatic Image Colorization Using GANs“. In Soft Computing and its Engineering Applications, 15–26. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0708-0_2.
Der volle Inhalt der QuelleCharpiat, Guillaume, Matthias Hofmann und Bernhard Schölkopf. „Automatic Image Colorization Via Multimodal Predictions“. In Lecture Notes in Computer Science, 126–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88690-7_10.
Der volle Inhalt der QuelleDing, Xiaowei, Yi Xu, Lei Deng und Xiaokang Yang. „Colorization Using Quaternion Algebra with Automatic Scribble Generation“. In Lecture Notes in Computer Science, 103–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27355-1_12.
Der volle Inhalt der QuelleGolyadkin, Maksim, und Ilya Makarov. „Semi-automatic Manga Colorization Using Conditional Adversarial Networks“. In Lecture Notes in Computer Science, 230–42. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72610-2_17.
Der volle Inhalt der QuelleKouzouglidis, Panagiotis, Giorgos Sfikas und Christophoros Nikou. „Automatic Video Colorization Using 3D Conditional Generative Adversarial Networks“. In Advances in Visual Computing, 209–18. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33720-9_16.
Der volle Inhalt der QuelleLee, Hyejin, Daehee Kim, Daeun Lee, Jinkyu Kim und Jaekoo Lee. „Bridging the Domain Gap Towards Generalization in Automatic Colorization“. In Lecture Notes in Computer Science, 527–43. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19790-1_32.
Der volle Inhalt der QuelleMouzon, Thomas, Fabien Pierre und Marie-Odile Berger. „Joint CNN and Variational Model for Fully-Automatic Image Colorization“. In Lecture Notes in Computer Science, 535–46. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22368-7_42.
Der volle Inhalt der QuelleDai, Jiawu, Bin Jiang, Chao Yang, Lin Sun und Bolin Zhang. „Local Pyramid Attention and Spatial Semantic Modulation for Automatic Image Colorization“. In Big Data, 165–81. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9709-8_12.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Automatic Colorization"
Watanabe, Taiki, Seitaro Shinagawa, Takuya Funatomi, Akinobu Maejima, Yasuhiro Mukaigawa, Satoshi Nakamura und Hiroyuki Kubo. „Improved Automatic Colorization by Optimal Pre-colorization“. In SIGGRAPH '23: Special Interest Group on Computer Graphics and Interactive Techniques Conference. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3588028.3603669.
Der volle Inhalt der QuelleŚluzek, Andrzej. „On Unguided Automatic Colorization of Monochrome Images“. In WSCG 2023 – 31. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision. University of West Bohemia, Czech Republic, 2023. http://dx.doi.org/10.24132/csrn.3301.38.
Der volle Inhalt der QuelleThasarathan, Harrish, Kamyar Nazeri und Mehran Ebrahimi. „Automatic Temporally Coherent Video Colorization“. In 2019 16th Conference on Computer and Robot Vision (CRV). IEEE, 2019. http://dx.doi.org/10.1109/crv.2019.00033.
Der volle Inhalt der QuelleKonovalov, Vitaly. „Method for automatic cartoon colorization“. In 2023 IX International Conference on Information Technology and Nanotechnology (ITNT). IEEE, 2023. http://dx.doi.org/10.1109/itnt57377.2023.10139184.
Der volle Inhalt der QuelleAbdulHalim, Mayada F., und Zaineb A. Mejbil. „Automatic colorization without human intervention“. In 2008 International Conference on Computer and Communication Engineering (ICCCE). IEEE, 2008. http://dx.doi.org/10.1109/iccce.2008.4580569.
Der volle Inhalt der QuelleLal, Shamit, Vineet Garg und Om Prakash Verma. „Automatic Image Colorization Using Adversarial Training“. In the 9th International Conference. New York, New York, USA: ACM Press, 2017. http://dx.doi.org/10.1145/3163080.3163104.
Der volle Inhalt der QuelleDeshpande, Aditya, Jason Rock und David Forsyth. „Learning Large-Scale Automatic Image Colorization“. In 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015. http://dx.doi.org/10.1109/iccv.2015.72.
Der volle Inhalt der QuelleLu, Yurong, Xianglin Huang, Yan Zhai, Lifang Yang und Yirui Wang. „ColorGAN: Automatic Image Colorization with GAN“. In 2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). IEEE, 2023. http://dx.doi.org/10.1109/iciba56860.2023.10164924.
Der volle Inhalt der QuelleGoel, Divyansh, Sakshi Jain, Dinesh Kumar Vishwakarma und Aryan Bansal. „Automatic Image Colorization using U-Net“. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2021. http://dx.doi.org/10.1109/icccnt51525.2021.9580001.
Der volle Inhalt der QuelleNguyen, Van, Vicky Sintunata und Terumasa Aoki. „Automatic Image Colorization based on Feature Lines“. In International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2016. http://dx.doi.org/10.5220/0005676401260133.
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