access icon free Physics-based dynamic texture analysis and synthesis model using GPU

The texture is a repetition of a particular structure. Textures classified into static texture and dynamic texture. There are two approaches for synthesising dynamic textures: image-based approach and physics-based approach. In the proposed work, the synthesis of different dynamic textures is shown. The physics-based approach is used to synthesise dynamic texture videos. Different mathematical models are proposed which are suitable to give appropriate motion to the dynamic textures. The raw frames are created, and these frames are further used to synthesise the dynamic textures using physics laws and mathematical formulae. The flexibility of each model is demonstrated. The proposed model has less specificity and less computational complexity. The proposed algorithms are implemented on the graphics processing unit to reduce the overall execution time and time complexity. High-quality videos are produced, and the evaluation of every frame assures the quality.

Inspec keywords: computational complexity; video signal processing; graphics processing units; image texture

Other keywords: physics-based dynamic texture analysis; image-based approach; different dynamic textures; static texture; dynamic texture videos; physics-based approach

Subjects: Optical, image and video signal processing; Video signal processing; Graphics techniques; Computer vision and image processing techniques

References

    1. 1)
      • 21. http://www.nvidia.in/object/product-geforce-gt-525m-in.html.
    2. 2)
      • 18. Kumar, T., Verma, K.: ‘A theory based on conversion of RGB image to gray image’, Int. J. Comput. Appl., 2010, 7, (2), pp. 710.
    3. 3)
      • 19. Nishad, P., Chezian, R.: ‘Various colour spaces and colour space conversion algorithms’, J. Global Res. Comput. Sci., 2013, 4, (1), pp. 4448.
    4. 4)
      • 7. Costantini, R.: ‘Compact representation for static and dynamic texture synthesis’. PhD thesis, 2006, pp. 1160.
    5. 5)
      • 14. Ghadekar, P., Chopade, N.: ‘Content-based dynamic texture analysis and synthesis based on SPIHT with GPU’, J. Inform. Process. Syst. (JIPS), 2016, 12, (1), pp. 4656.
    6. 6)
      • 2. https://www.youtube.com/user/NewWorldOps.
    7. 7)
      • 20. http://www.nvidia.com/object/what-is-gpu-computing.html.
    8. 8)
      • 3. Reeves, W.: ‘Particle systems: a technique for modeling a class of fuzzy objects’, ACM Transactions on Graphics, 1983, 2, (2), pp. 91108.
    9. 9)
      • 22. Smith, A.: ‘Color gamut transform pairs’, Proc. Int. Conf. SIGGRAPH, 1978, 12, (3), pp. 1219.
    10. 10)
      • 8. Szummer, M., Picard, R.: ‘Temporal texture modeling’. IEEE Int. Conf. on Image Processing, Lausanne, Switzerland, 1996, pp. 823826.
    11. 11)
      • 11. You, X., Guo, W., Yu, S., et al: ‘Kernel learning for dynamic texture synthesis’, IEEE Trans. Image Process., 2016, 25, (10), pp. 47824795.
    12. 12)
      • 5. Fournier, A., Reeves, W.: ‘A simple model of ocean waves’, ACM Proc. SIGGRAPH, 1986, 20, (4), pp. 7584.
    13. 13)
      • 15. Ghadekar, P., Chopade, N.: ‘Modelling nonlinear dynamic textures using hybrid DWT-DCT and kernel PCA with GPU’, J. Inst. Eng. Series-B (Springer Journal), 2016, 97, (4), pp. 549555.
    14. 14)
      • 9. Kwatra, V., Essa, I., Turk, G., et al: ‘Graphcut textures: image and video synthesis using graphcut’, Georgia Inst. Technol., 2003, 22, pp. 277286.
    15. 15)
      • 1. http://product.corel.com/help/CorelDRAW/540229932/Main/EN/User-Guide/Corel DRAW-X7.pdf.
    16. 16)
      • 16. https://www.youtube.com/watch?v=IDuvQu1uczo.
    17. 17)
      • 12. Chubach, O., Garus, P., et al: ‘Synthesis coding of dynamic textures based on motion distribution statistics’. IEEE Int. Conf. on Image Processing (ICIP), Beijing, People's Republic of China, 2017, pp. 30163020.
    18. 18)
      • 4. Harrington, S.: ‘Computer graphics’ (McGraw-Hill Publication, USA, 1983, 2nd edn.).
    19. 19)
      • 6. Doretto, G., Soatto, S.: ‘Editable dynamic textures’. ACM CVPR, Washington, DC, USA, 2003, vol. 2, pp. 137142.
    20. 20)
      • 13. Yang, F., Xia, G., et al: ‘Stationary dynamic texture synthesis using convolutional neural networks’. Int. Conf. on Signal Processing Proc. (ICSP), Chengdu, People's Republic of China, 2017, pp. 11351139.
    21. 21)
      • 10. Rahman, A., Tasnim, S.: ‘Block motion-based dynamic texture analysis: a review’, Int. J. Comput. Trends Technol., 2014, 8, (2), pp. 7678.
    22. 22)
      • 17. https://www.youtube.com/watch?v=zJrZFANxn7s.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2019.0984
Loading

Related content

content/journals/10.1049/iet-ipr.2019.0984
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading