Шляхи підвищення ефективності сучасних методів матування зображень

Автор(и)

  • Дмитро Дмитрович Луп’як Вінницький національний технічний університет
  • Роман Наумович Квєтний Вінницький національний технічний університет

DOI:

https://doi.org/10.31649/1681-7893-2021-41-1-33-38

Ключові слова:

глибоке навчання, матування, сегментація, комп’ютерний зір

Анотація

В роботі проведено аналітичний огляд сучасних методів матування зображення. Визначено ключові ідеї методів, що існують та наведено порівняння їхньої точності. Визначено проблеми та завдання для подальшого дослідження методів матування зображення

Посилання

Y. Mishima, “Soft edge chroma-key generation based upon hexoctahedral color space,” in U.S. Patent 5,355,174, 1993.

Lupiak D. D., «Vykorystannia metodiv sehmentatsii ta matuvannia v zadachi vyznachennia perednoho planu zobrazhennia» Materialy konferentsii «L Naukovo-tekhnichna konferentsiia pidrozdiliv Vinnytskoho natsionalnoho tekhnichnoho universytetu (2021)», Vinnytsia, 2021. [Elektronnyi resurs]. Rezhym dostupu: https://conferences.vntu.edu.ua/index.php/allvntu/index/pages/view/zbirn2021

Bilynskyi Y. Y. Metod sehmentatsii ultrazvukovykh zobrazhen na osnovi analizu lokalnoi statystyky histohram / Bilynskyi Y.I., Melnychuk A.O.,Chumak O.A.// Visnyk Vinnytskoho politekhnichnoho instytutu 2010. – №6. – S.102-107.

Yung-Yu Chuang, Brian Curless, David Salesin, and Richard Szeliski. A bayesian approach to digital matting. In 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), with CD-ROM, 8-14 December 2001, Kauai, HI, USA, pages 264–271, 2001.

Jue Wang and Michael F. Cohen. An iterative optimization approach for unified image segmentation and matting. In 10th IEEE International Conference on Computer Vision (ICCV 2005), 17-20 October 2005, Beijing, China, pages 936–943, 2005.

Eduardo Simoes Lopes Gastal and Manuel M. Oliveira. Shared sampling for realtime alpha matting. Comput. Graph. Forum, 29(2):575–584, 2010.

Kaiming He, Christoph Rhemann, Carsten Rother, Xiaoou Tang, and Jian Sun. A global sampling method for alpha matting. In The 24th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011, Colorado Springs, CO, USA, 2011.

Xiaoxue Feng, Xiaohui Liang, and Zili Zhang. A cluster sampling method for image matting via sparse coding. In Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, Proceedings, Part II, pages 204–219, 2016.

Jian Sun, Jiaya Jia, Chi-Keung Tang, and Heung-Yeung Shum. Poisson matting. ACM Trans. Graph., 23(3):315–321, 2004.

Leo Grady, Thomas Schiwietz, Shmuel Aharon, and RÃijdiger Westermann. Random walks for interactive alpha-matting. In IN PROCEEDINGS OF VIIP 2005, pages 423– 429, 2005

Yung-Yu Chuang, Brian Curless, David Salesin, and Richard Szeliski. A bayesian approach to digital matting. In 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), Kauai, HI, USA, pages 264–271, 2001.

Anat Levin, Alex Rav-Acha, and Dani Lischinski. Spectral matting. In 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), 18-23 June 2007, Minneapolis, Minnesota, USA, 2007.

Anat Levin, Dani Lischinski, and Yair Weiss. A closed-form solution to natural image matting. IEEE Trans. Pattern Anal. Mach. Intell., 30(2):228–242, 2008.

Yuanjie Zheng, Chandra Kambhamettu, Jingyi Yu, Thomas L. Bauer, and Karl V. Steiner. Fuzzymatte: A computationally efficient scheme for interactive matting. In 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), 24-26 June 2008, Anchorage, Alaska, USA, 2008.

J. Wang and M. Cohen, “Optimized color sampling for robust matting,” in Proc. of IEEE CVPR, 2007.

Donghyeon Cho, Yu-Wing Tai, and Inso Kweon. Natural image matting using deep convolutional neural networks. In European Conference on Computer Vision, pages 626–643. Springer, 2016

Ning Xu, Brian Price, Scott Cohen, and Thomas Huang. Deep image matting. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2970– 2979, 2017

Hao Lu, Yutong Dai, Chunhua Shen, and Songcen Xu. Indices matter: Learning to index for deep image matting. In Proceedings of the IEEE International Conference on Computer Vision, pages 3266–3275, 2019

Sebastian Lutz, Konstantinos Amplianitis, and Aljosa Smolic. Alphagan: Generative adversarial networks for natural image matting. arXiv preprint arXiv:1807.10088, 2018

Jingwei Tang, Yagiz Aksoy, Cengiz Oztireli, Markus Gross, and Tunc Ozan Aydin. Learning-based sampling for natural image matting. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3055– 3063, 2019

Yaoyi Li and Hongtao Lu. Natural image matting via guided contextual attention. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 11450–11457, 2020

Haichao Yu, Ning Xu, Zilong Huang, Yuqian Zhou, and Humphrey Shi. High-resolution deep image matting. arXiv preprint arXiv:2009.06613, 2020

Y. Wang, Y. Niu, P. Duan, J. Lin, and Y. Zheng. Deep propagation based image matting.

Hou, Q., Liu, F.: Context-aware image matting for simultaneous foreground and alpha estimation. In: Proceedings of the International Conference on Computer Vision (October 2019)

Cai, S., Zhang, X., Fan, H., Huang, H., Liu, J., Liu, J., Liu, J., Wang, J., Sun, J.: Disentangled image matting. In: Proceedings of the International Conference on Computer Vision (October 2019)

Marco Forte and Franc¸ois Pitie. F,b, alpha matting. ´ arXiv preprint arXiv:2003.07711, 2020

Yaoyi Li, Qingyao Xu, and Hongtao Lu. Hierarchical opacity propagation for image matting. arXiv preprint arXiv:2004.03249, 2020

Yu Qiao, Yuhao Liu, Xin Yang, Dongsheng Zhou, Mingliang Xu, Qiang Zhang, and Xiaopeng Wei. Attention-guided hierarchical structure aggregation for image matting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13676–13685, 2020

Yuhao Liu, Jiake Xie, Yu Qiao. Prior-Induced Information Alignment for Image Matting. arXiv:2106.14439v1, 2021

H. Lu, Y. Dai, C. Shen, and S. Xu, “Index networks,” IEEE Trans. Pattern Anal. Mach. Intell., 2020.

Yanan Sun, Chi-Keung Tang, Yu-Wing Tai. Semantic Image Matting. arXiv:2104.08201v1, 2021

Soumyadip Sengupta, Vivek Jayaram, Brian Curless, Steven M Seitz, and Ira Kemelmacher-Shlizerman. Background matting: The world is your green screen. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2291–2300, 2020

Shanchuan Lin, Andrey Ryabtsev, Soumyadip Sengupta, Brian Curless, Steve Seitz, and Ira KemelmacherShlizerman. Real-time high-resolution background matting. arXiv preprint arXiv:2012.07810, 2020

Xiaoyong Shen, Xin Tao, Hongyun Gao, Chao Zhou, and Jiaya Jia. Deep automatic portrait matting. In European Conference on Computer Vision, pages 92–107. Springer, 2016

Yagiz Aksoy, Tae-Hyun Oh, Sylvain Paris, Marc Pollefeys, ˘ and Wojciech Matusik. Semantic soft segmentation. ACM Transactions on Graphics (TOG), 37(4):1–13, 2018

Quan Chen, Tiezheng Ge, Yanyu Xu, Zhiqiang Zhang, Xinxin Yang, and Kun Gai. Semantic human matting. In 2018 ACM Multimedia Conference on Multimedia Conference, pages 618–626. ACM, 2018

Guanying Chen, Kai Han, and Kwan-Yee K. Wong. Tom-net: Learning transparent object matting from a single image. In CVPR, 2018

Yunke Zhang, Lixue Gong, Lubin Fan, Peiran Ren, Qixing Huang, Hujun Bao, and Weiwei Xu. A late fusion cnn for digital matting. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 7469– 7478, 2019

Zhanghan Ke, Kaican Li, Yurou Zhou, Qiuhua Wu, Xiangyu Mao, Qiong Yan. Is a Green Screen Really Necessary for Real-Time Portrait Matting? arXiv:2011.11961v2, 2020

Jinlin Liu, Yuan Yao, Wendi Hou, Miaomiao Cui, Xuansong Xie, Changshui Zhang, and Xian-sheng Hua. Boosting semantic human matting with coarse annotations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8563–8572, 2020.

Yuhongze Zhou, Liguang Zhou, Tin Lun Lam, Yangsheng Xu. Semantic-guided Automatic Natural Image Matting with Light-weight Non-local Attention. arXiv:2103.17020v2, 2021

Jizhizi Li, Jing Zhang. Deep Automatic Natural Image Matting. arXiv:2107.07235v1, 202

Piddubetska M.P. Osoblyvosti vykorystannia neironnykh merezh v zadachakh obrobky hrafichnykh zobrazhen / Piddubetska M.P., Romaniuk O.N., Tymchenko L.I. // Elektronni informatsiini resursy: stvorennia, vykorystannia, dostup // Zbirnyk materialiv Mizhnarodnoi naukovo-praktychnoi Internet-konferentsii, m.Vinnytsia, 2015. [Elektronnyi resurs]. Rezhym dostupu: http://conf.vntu.edu.ua/eiron/2014

Martyniuk T. B. Analiz tendentsii rozvytku suchasnykh komp`iuternykh system / T. B. Martyniuk, A. V. Kozhem`iako, L. M. Krupershtein // Optyko-elektronni informatsiino-enerhetychni tekhnolohii. – 2016. – № 2. – S. 5-13.

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Переглядів анотації: 123

Опубліковано

2021-07-28

Як цитувати

[1]
Д. Д. Луп’як і Р. Н. Квєтний, «Шляхи підвищення ефективності сучасних методів матування зображень», Опт-ел. інф-енерг. техн., вип. 41, вип. 1, с. 33–38, Лип 2021.

Номер

Розділ

Методи та системи оптико-електронної і цифрової обробки зображень та сигналів

Метрики

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