PL EN


Preferences help
enabled [disable] Abstract
Number of results
2019 | 119 | 85-96
Article title

Actual trends in modern creative photography

Content
Title variants
Languages of publication
EN
Abstracts
EN
The main tendencies in modern creative photography which have particular importance for studying the processes of modern culture are considered. Popular styles in modern photography are also analyzed and reviewed, current situations in the field of photography research are described, and the ways of its development are outlined.
Keywords
Year
Volume
119
Pages
85-96
Physical description
Contributors
  • Department of Systems and Applications of TV Studios, Tashkent University of Information Technologies named after Muhammad al-Khorezmi, Amir Temur Street 108, Tashkent city, 100084, Uzbekistan
References
  • [1] J. E. Kyprianidis, J. Collomosse, T. Wang, and T. Isenberg, State of the “Art: a taxonomy of artistic stylization techniques for images and video, IEEE Transactions on Visualization and Computer Graphics, vol. 19, no. 5, pp. 866–885, 2013.
  • [2] D. Chi, A natural image pointillism with controlled ellipse dots, Advances in Multimedia, vol. 2014, Article ID 567846, 17 pages, 2014.
  • [3] W. Qian, D. Xu, K. Yue, Z. Guan, Y. Pu, and Y. Shi, Gourd pyrography art simulating based on non-photorealistic rendering, Multimedia Tools and Applications, vol. 76, no. 13, pp. 14559–14579, 2017.
  • [4] R. H. Kazi, K.-C. Chua, S. Zhao, R. C. Davis, and K.-L. Low, Sand Canvas: A multi-touch art medium inspired by sand animation, Proceedings of the 29th Annual CHI Conference on Human Factors in Computing Systems, CHI 2011, pp. 1283–1292, Canada, May 2011.
  • [5] C.-F. Lin and C.-S. Fuh, Uncle sand: A sand drawing application in ipad, Proceeding of Computer Vision, Graphics, and Image Processing Conference, Nantou, Taiwan, 2012.
  • [6] G. Song and K.-H. Yoon, Sand image replicating sand animation process, Proceedings of the 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision, FCV 2013, pp. 74–77, Republic of Korea, February 2013.
  • [7] M. Yang, X. He, C. Hu, T. Wang, and G. Yang, Algorithm for interactive simulation of sand painting, Journal of Computer-Aided Design and Computer Graphics, vol. 28, no. 7, pp. 1084–1093, 2016.
  • [8] X. Xiaochen, K. Liqun, H. Xie, and Y. Xiaowen, Sand painting gesture recognition based on multi-touch, Computer Engineering and Applications, vol. 53, no. 1, pp. 244–248, 2017.
  • [9] H. Fan, Z. Chen, and J. Li, Image sand stylepainting algorithm, Applied Mathematics & Information Sciences, vol. 8, no. 2, pp. 765–771, 2014.
  • [10] M. Ura, M. Yamada, M. Endo, S. Miyazaki, and T. Yasuda, A paint tool for image generation of sand animation style, Human Interface, vol. 11, no. 21, pp. 7–12, 2009.
  • [11] T. Wu, J. Yang, and G. Ran, Computational aesthetics analysis on sand painting style, Journal of Frontiers of Computer Science and Technology, vol. 10, no. 7, pp. 1021–1034, 2016.
  • [12] H. Winnemöller, S. C. Olsen, and B. Gooch, Real-time video abstraction, ACM Transactions on Graphics, vol. 25, no. 3, pp. 1221–1226, 2006.
  • [13] G. Papari, N. Petkov, and P. Campisi, Artistic edge and corner enhancing smoothing, IEEE Transactions on Image Processing, vol. 16, no. 10, pp. 2449–2462, 2007.
  • [14] J. E. Kyprianidis, H. Kang, and J. Döllner, Image and video abstraction by anisotropic Kuwahara filtering, Computer Graphics Forum, vol. 28, no. 7, pp. 1955–1963, 2009.
  • [15] E. Arias-Castro and D. L. Donoho, Does median filtering truly preserve edges better than linear filtering? The Annals of Statistics, vol. 37, no. 3, pp. 1172–1206, 2009.
  • [16] P. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 629–639, 1990.
  • [17] D. Li and Y. Du, Artificial Intelligence with Uncertainty, Chapman & Hall, Boca Raton, Fla, USA, 2007.
  • [18] S. Wang, Wenyan Gan, D. Li, and D. Li, Data field for hierarchical clustering, International Journal of Data Warehousing and Mining, vol. 7, no. 4, pp. 43–63, 2011.
  • [19] S. Wang, J. Fan, M. Fang, and H. Yuan, HGCUDF: Hierarchical grid clustering using data field, Journal of Electronics, vol. 23, no. 1, pp. 37–42, 2014.
  • [20] S. Wang and Y. Chen, HASTA: A hierarchical-grid clustering algorithm with data field, International Journal of Data Warehousing and Mining, vol. 10, no. 2, pp. 39–54, 2014.
  • [21] J. Zhao and M. Jia, Segmentation algorithm for small targets based on improved data field and fuzzy c-means clustering, Optik - International Journal for Light and Electron Optics, vol. 126, no. 23, pp. 4330–4336, 2015.
  • [22] T. Wu, Image data field-based framework for image thresholding, Optics & Laser Technology, vol. 62, pp. 1–11, 2014.
  • [23] P. L. Rosin, Unimodal thresholding, Pattern Recognition, vol. 34, no. 11, pp. 2083–2096, 2001.
  • [24] R. E. W. Gonzalez and S. L. C. R. Eddins, Digital Image Processing Using MATLAB, Gatesmark Publishing, 2009.
  • [25] D. Mould and P. L. Rosin, Abenchmark image set for evaluating stylization,” in Proceedings of the Joint Symposium on Computational Aesthetics and Sketch Based Interfaces and Modeling 18 Mathematical Problems in Engineering and Non-Photorealistic Animation and Rendering, Expresive 16, Eurographics Association, pp. 11–20, Aire-la-Ville, Switzerland, 2016.
  • [26] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, Image quality assessment: fromerror visibility to structural similarity, IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600–612, 2004.
  • [27] L. Zhang, L. Zhang, X. Mou, and D. Zhang, FSIM: a feature similarity index for image quality assessment, IEEE Transactions on Image Processing, vol. 20, no. 8, pp. 2378–2386, 2011.
  • [28] L. Zhang, Y. Shen, and H. Li, VSI: a visual saliency-induced index for perceptual image quality assessment, IEEE Transactions on Image Processing, vol. 23, no. 10, pp. 4270–4281, 2014.
  • [29] A. Liu, W. Lin, and M. Narwaria, Image quality assessment based on gradient similarity, IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 1500–1512, 2012.
  • [30] W. Xue, L. Zhang, X. Mou, and A. Bovik, Gradient magnitude similarity deviation: a highly efficient perceptual image quality index, IEEE Transactions on Image Processing, vol. 23, no. 2, pp. 684–695, 2014.
  • [31] M. Abdellah, A. Eldeib, and A. Sharawi, High performance GPU-Based Fourier volume rendering, International Journal of Biomedical Imaging, vol. 2015, Article ID 590727, 13 pages, 2015.
Document Type
article
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.psjd-c9462141-11d6-4656-b432-891d15b7c843
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.