Skip to main content
SpringerLink
Log in

Enhancement method for edge texture details of the filmic and visual three-dimensional animation

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Enhancement method for edge texture details of the filmic and visual three-dimensional animation has the vital significance to the dynamic analysis and evaluation of the following images. The traditional enhancement method for edge texture detail mainly uses fuzzy contrast to improve the quality of animation. The contrast and clarity are poor. In order to reduce the noise, this paper proposes the enhancement method of filmic and visual three-dimensional animation edge texture detail based on statistical shape priors. Firstly, this method carries out the segmentation, de-noising, edge detection processing on animation, then uses statistical shape prior method to enhance the edge texture detail. Experimental results show that the proposed method can obtain more ideal edge detail information.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Abdel-Basset M, Fakhry AE, El-henawy I, Qiu T, Sangaiah AK (2017) Feature and intensity based medical image registration using particle swarm optimization. J Med Syst 41(12):197. https://doi.org/10.1007/s10916-017-0846-9

    Article  Google Scholar 

  2. Amitrano D, Belfiore V, Cecinati F et al (2016) Urban areas enhancement in multitemporal SAR RGB images using adaptive coherence window and texture information[J]. IEEE J-STARS 9(8):3740–3752

    Google Scholar 

  3. Chang YJ, Ho YS (2016) Disparity map enhancement in pixel based stereo matching method using distance transform[J]. J Vis Commun Image Represent 40(5):118–127

    Article  Google Scholar 

  4. Chichun P et al (2018) Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU. J Comput Sci-Neth 28:1–10

    Article  MathSciNet  Google Scholar 

  5. Couture RA, Dymek RF (2015) A reexamination of absorption and enhancement effects in X-ray fluorescence trace element analysis[J]. Am Mineral 81(5–6):639–650

    Google Scholar 

  6. Dixit T, Palani IA, Singh V (2016) Selective tuning of enhancement in near band edge emission in hydrothermally grown ZnO nanorods coated with gold[J]. J Lumin 170(7):180–186

    Article  Google Scholar 

  7. Guerra M, Jozsef G (2016) SU-F-T-93: breast surface dose enhancement using a clinical prone breast board[J]. Med Phys 43(6):3483–3483

    Article  Google Scholar 

  8. Harris JR, Jensen KL, Shiffler DA (2016) Edge enhancement control in linear arrays of ungated field emitters[J]. J Appl Phys 119(4):043301

    Article  Google Scholar 

  9. Ip AH, Kiani A, Kramer IJ et al (2015) Infrared colloidal quantum dot photovoltaics via coupling enhancement and agglomeration suppression[J]. ACS Nano 9(9):8833–8842

    Article  Google Scholar 

  10. Lin Y, Zhu X, Zheng Z et al (2017) The individual identification method of wireless device based on dimensionality reduction and machine learning. J Supercomput 5:1–18. https://doi.org/10.1007/s11227-017-2216-2

    Article  Google Scholar 

  11. Liu S, Zhang Z, Qi L et al (2016) A fractal image encoding method based on statistical loss used in agricultural image compression. Multimed Tools Appl 75(23):15525–15536

    Article  Google Scholar 

  12. Liu S, Pan Z, Cheng X (2017) A novel fast fractal image compression method based on distance clustering in high dimensional sphere surface. Fractals 25(4):1740004

    Article  Google Scholar 

  13. Liu S, Lu M, Liu G (2017) A novel distance metric: generalized relative entropy. Entropy 19(6):269

    Article  Google Scholar 

  14. Liu S, Bai W, Liu G et al (2018) Parallel fractal compression method for big video data. Complexity 2018:2016976. https://doi.org/10.1155/2018/2016976

    Article  MATH  Google Scholar 

  15. Mengye L, Shuai L (2018) Nucleosome positioning based on generalized relative entropy. Soft Computing. Accepted. https://doi.org/10.1007/s00500-018-3602-2

  16. Peng H, Li B, Ling H et al (2017) Salient object detection via structured matrix decomposition[J]. IEEE Trans Pattern Anal Mach Intell 39(4):818–832

    Article  Google Scholar 

  17. Shuai L, Weina F, Liqiang H et al (2017) Distribution of primary additional errors in fractal encoding method. Multimed Tools Appl 76(4):5787–5802

    Article  Google Scholar 

  18. Sun Y, Yang W, Zeng X et al (2016) Edge enhancement of potential field data using spectral moments[J]. Geophysics 81(1):G1–G11

    Article  Google Scholar 

  19. Trabelsi RB, Masmoudi AD, Masmoudi DS (2016) Hand vein recognition system with circular difference and statistical directional patterns based on an artificial neural network[J]. Multimed Tools Appl 75(2):687–707

    Article  Google Scholar 

  20. Wang K (2016) Directional and omnidirectional edge enhancement based on radial Hilbert transform of Gabor filter[J]. Electron Lett 52(9):701–703

    Article  Google Scholar 

  21. Zhang B, Chen Z, Sun H et al (2016) Vectorial optical vortex filtering for edge enhancement[J]. J Opt 18(3):035703

    Article  Google Scholar 

  22. Zheng P, Shuai L, Arun S, Khan M (2018) Visual attention feature (VAF) : a novel strategy for visual tracking based on cloud platform in intelligent surveillance systems. J Parallel Distr Com 120:182–194

    Article  Google Scholar 

  23. Zhou K, Zhou L, Liu T, et al (2016) Based on the improved canny operator real-time video edge detection system on FPGA [J]. The design and implementation of computer measurement and control 24(1):219–222

Download references

Acknowledgements

This research is supported by following grants: Natural Science Foundation of Inner Mongolia [No. 2018MS6010]; Foundation Science Research Start-up Fund of Inner Mongolia Agriculture University. [JC2016005]; Scientific Research Foundation for Doctors of Inner Mongolia Agriculture University. [NDYB2016-11].

Author information

Authors and Affiliations

  1. Changzhou Textile Garment Institute, Changzhou, 213000, China

    Hao Su

  2. College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, 010012, China

    Weina Fu

Authors
  1. Hao Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weina Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Weina Fu.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Su, H., Fu, W. Enhancement method for edge texture details of the filmic and visual three-dimensional animation. Multimed Tools Appl 79, 16351–16367 (2020). https://doi.org/10.1007/s11042-019-7319-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-7319-8

Keywords

Search

Navigation