Do You Like Sclera? Sclera-region Detection and Colorization for Anime Character Line Drawings
Colorizing line drawings requires special skill, experience, and knowledge. Artists also spend a great deal of time and effort creating art. Given this background, research on automated line drawing colorization was recently conducted. However, the existing approaches present multiple problems, one...
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| Published in | The International journal of networked and distributed computing (Online) Vol. 7; no. 3; pp. 113 - 120 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Netherlands
2019
Springer |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2211-7938 2211-7946 2211-7946 |
| DOI | 10.2991/ijndc.k.190711.001 |
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| Abstract | Colorizing line drawings requires special skill, experience, and knowledge. Artists also spend a great deal of time and effort creating art. Given this background, research on automated line drawing colorization was recently conducted. However, the existing approaches present multiple problems, one of which is the inconsistency of the whites of the eyes (sclera) between line drawings and the results of colorizing. In particular, in line drawings, a person’s skin and sclera are often expressed in white. Hence, there are cases in which existing colorization methods cannot predict the boundary correctly. In this study, we propose automated colorization methods that use machine learning to segment sclera regions in grayscale line drawings. To improve the accuracy of previous automated colorization approaches, we implemented sclera-region detection and an automated colorizing approach on grayscale line drawings of people. In addition, we evaluated the colorization results created by our methods through a user study. Statistics show that our methods are somewhat superior to industrial application, but many of our respondents perceived little difference between the methods. |
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| AbstractList | Colorizing line drawings requires special skill, experience, and knowledge. Artists also spend a great deal of time and effort creating art. Given this background, research on automated line drawing colorization was recently conducted. However, the existing approaches present multiple problems, one of which is the inconsistency of the whites of the eyes (sclera) between line drawings and the results of colorizing. In particular, in line drawings, a person’s skin and sclera are often expressed in white. Hence, there are cases in which existing colorization methods cannot predict the boundary correctly. In this study, we propose automated colorization methods that use machine learning to segment sclera regions in grayscale line drawings. To improve the accuracy of previous automated colorization approaches, we implemented sclera-region detection and an automated colorizing approach on grayscale line drawings of people. In addition, we evaluated the colorization results created by our methods through a user study. Statistics show that our methods are somewhat superior to industrial application, but many of our respondents perceived little difference between the methods. |
| Author | Tahara, Yasuyuki Ohsuga, Akihiko Orihara, Ryohei Aizawa, Masashi Sei, Yuichi |
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| Keywords | segmentation sclera region colorization Line drawing |
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| References_xml | – reference: C. Li, X. Liu, T-T. Wong, Deep extraction of manga structural lines, ACM Trans. Graph. 36 (2017). – reference: R. Zhang, P. Isola, A.A. Efros, Colorful image colorization, in: B. Leibe, J. Matas, N. Sebe, M. Welling (Eds.), Computer Vision — ECCV 2016, Springer International Publishing, Cham, 2016, pp. 649–666. – reference: P. Sangkloy, J. Lu, C. Fang, F. Yu, J. Hays, Scribbler: controlling deep image synthesis with sketch and color, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Honolulu, HI, USA, 2017. – reference: S. Iizuka, E. Simo-Serra, H. Ishikawa, Let there be color!: Joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification, ACM Trans. Graph. 35 (2016). – reference: E. Simo-Serra, S. Iizuka, H. Ishikawa, Mastering sketching: adversarial augmentation for structured prediction, ACM Trans. Graph. 37 (2018). – reference: hepesu, LineDistiller, https://github.com/hepesu/LineDistiller. – reference: G. Larsson, M. Maire, G. Shakhnarovich, Learning representations for automatic colorization, in: B. Leibe, J. Matas, N. Sebe, M. Welling (Eds.), Computer Vision — ECCV 2016, Springer International Publishing, Cham, 2016, pp. 577–593. – reference: Y. Liu, Z. Qin, Z. Luo, H. Wang, Auto-painter: Cartoon image generation from sketch by using conditional generative adversarial networks, http://arxiv.org/abs/1705.01908. – reference: E. Simo-Serra, S. Iizuka, K. Sasaki, H. Ishikawa, Learning to simplify: fully convolutional networks for rough sketch cleanup. ACM Trans. Graph. 35 (2016). – reference: Y. Ci, X. Ma, Z. Wang, H. Li, Z. Luo, User-guided deep anime line art colorization with conditional adversarial networks, 26th ACM International Conference on Multimedia, ACM, Seoul, Republic of Korea, 2018, pp. 1536–1544. – reference: P. Hensman, K. Aizawa, cGAN-based manga colorization using a single training image, 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), IEEE, Kyoto, Japan, 2017, pp. 72–77. – reference: nagadomi, lbpcascade_animeface, https://github.com/nagadomi/lbpcascade_animeface. – reference: O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation, in: N. Navab, J. Hornegger, W Wells, A. Frangi (Eds.), Medical Image Computing and Computer-Assisted Intervention — MICCAI 2015, Springer International Publishing, Cham, 2015, pp. 234–241. – reference: SykoraDDinglianaJCollinsSLazyBrush: flexible painting tool for hand-drawn cartoonsComput. Graph. Forum200928599608 – reference: MatsuiYItoKAramakiYFujimotoAOgawaTYamasakiTSketch-based manga retrieval using Manga109 datasetMultimedia Tools and Applications2017792181121838 – reference: Y.Y. Boykov, M.-P. Jolly, Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images, Eighth IEEE International Conference on Computer Vision (ICCV), IEEE, Vancouver, BC, Canada, Canada, 2001, pp. 105–112. – reference: R. Zhang, J-Y Zhu, P. Isola, X. Geng, A.S. Lin, T. Yu, et al., Realtime user-guided image colorization with learned deep priors, ACM Trans. Graph. 36 (2017). – reference: L. Zhang, Y. Ji, X. Lin, C. Liu, Style transfer for anime sketches with enhanced residual U-net and auxiliary classifier GAN, 2017 4th IAPR Asian Conference on Pattern Recognition (ACPR), IEEE, Nanjing, China, 2017, pp. 506–511. – reference: L. Zhang, C. Li, T-T. Wong, Y Ji, C. Liu, Two-stage sketch colorization. ACM Trans. Graph. 37 (2018). – reference: K. Frans. Outline colorization through tandem adversarial networks, http://arxiv.org/abs/1704.08834. – reference: FurusawaCHiroshibaKOgakiKOdagiriYComicolorization: semi-automatic manga colorization2017Bangkok, ThailandSIGGRAPH Asia 2017 Technical Briefs – reference: Preferred Networks, Inc., PaintsChainer, https://paintschainer.preferred.tech/index_ja.html. – reference: LevinALischinskiDWeissYColorization using optimizationACM Trans. Graph.200423689694 – reference: RotherCKolmogorovVBlakeA“GrabCut”: interactive foreground extraction using iterated graph cutsACM Trans. Graph.200423309314 – reference: QuYWongT-THengP-AManga colorizationACM Trans. Graph.20062512141220 – reference: E. Simo-Serra, S. Iizuka, H. Ishikawa, Real-time data-driven interactive rough sketch inking, ACM Trans. Graph. 37 (2018). |
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| Title | Do You Like Sclera? Sclera-region Detection and Colorization for Anime Character Line Drawings |
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