Skip to main content
SpringerLink
Log in

Virtual reality of recognition technologies of the improved contour coding image based on level set and neural network models

  • Neural Computing in Next Generation Virtual Reality Technology
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Image contour-based feature extraction method has been applied to some fields of image recognition and virtual reality. However, image contour features are easily susceptible to factors like noise, rotation and thresholds during extraction and processing. To solve the above problem, this paper proposes a contour coding image recognition algorithm based on level set and BP neural network models. Firstly, level set model is employed to extract the contours of images. Secondly, image coding method proposed herein is used to code images horizontally, vertically and obliquely. At last, BP neural network model is trained to recognize the image codes. Validity of the proposed algorithm is verified by using a set of actual engineering part images as well as MPEG and PLANE databases. The results show that the proposed method achieves high recognition rate and requires small samples, which also exhibits good robustness to external disturbances such as noise and image scaling and rotation.

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

References

  1. Turk M, Pentland A (1991) Eigenfaxes for recognition. J Cogn Neurosci 3(1):71–86

    Article  Google Scholar 

  2. Wei W, Fan X, Song H et al (2016) Imperfect information dynamic stackelberg game based resource allocation using hidden markov for cloud computing. IEEE Trans Serv Comput. doi:10.1109/TSC.2016.2528246

    Google Scholar 

  3. Reed TR, Du Buf JMH (1993) A review of recent texture segmentation, feature extraction techniques. CVGIP Image Underst 57:359–372

    Article  Google Scholar 

  4. Chen CH, Pan LF, Wang PSP (1993) Handbook of pattern recognition and computer vision. World Seientific Publishing, Singapore, pp 235–276

    Book  Google Scholar 

  5. Brooks RA (1983) Model-based three-dimensional interpretations of two-dimensional images. Pattern Anal Mach Intell 5(2):140–150

    Article  MathSciNet  Google Scholar 

  6. Pope AR (1994) Model-based object recognition: a survey of recent research. Technical report

  7. Chin RT, Dyer CR (1986) Model-based recognition in robot vision. Comput Surv 18(1):67–108

    Article  Google Scholar 

  8. Brunelli R, Poggio T (1993) Face recognition: features versus templates. Pattern Anal Mach Intell 15(10):1042–1052

    Article  Google Scholar 

  9. Torres RS, Falco A, Costa F (2003) A graph-based approach for multiscale shape analysis. Pattern Recogn 7(6):1163–1174

    Google Scholar 

  10. Wang Z, Chi Z, Feng DD (2004) Shape based leaf image retrieval. IEEE Proc Vis Image Signal Process 150(1):34–43

    Article  Google Scholar 

  11. Sebastian TB, Klein PN, Kimia BB (2004) Recognition of shapes by editing their shock graphs. IEEE Trans Pattern Anal Mach Intell 26(5):550–571

    Article  Google Scholar 

  12. Orfanolu M, Gukberk B, Akarun I (2004) 3D shape-based face recognition using automatically registered facial surfaces. Pattern Recogn 4:183–186

    Google Scholar 

  13. Loy Gareth, Barnes Nick (2004) Fast shpe-based road sign detection for a driver assistance system. Intell Robots Syst 1:70–75

    Google Scholar 

  14. Pavlidids T (1998) Survey of shape analysis methods. Comput Graph Image Process 7(2):243–258

    Article  Google Scholar 

  15. Lv Z, Chen G, Zhong C et al (2012) A framework for multi-dimensional webgis based interactive online virtual community. Adv Sci Lett 7(1):215–219

    Article  Google Scholar 

  16. Mokhtarian F, Bober M (2003) Curvature scale space representation: theory, applications and MPEG-7 standardization. Springer Publishing, New York

    Book  MATH  Google Scholar 

  17. Mehere BM, Kankanhalli MS, Lee WF (1997) Shape measures for content based image retrieval: a comparison. Inf Process Manag 33(3):319–337

    Article  Google Scholar 

  18. Lv Z, Halawani A, Feng S et al (2014) Multimodal hand and foot gesture interaction for handheld devices. ACM Trans Multimed Comput Commun Appl (TOMM) 11(1s):10

    Google Scholar 

  19. Wu WY, Wang MJJ (1993) Detecting the dominant points by the curvature-based polygonal approximation. Graph Mod Image Process 55(2):79–88

    Article  Google Scholar 

  20. Chuang GGH, Kuo CCJ (1996) Wavelet descriptor of planar curves: theory and applications. IEEE Trans Image Process 5(1):56–70

    Article  Google Scholar 

  21. Tieng QM, Boles WW (1997) Recognition of 2D object contours using the wavelet transform zero-crossing representation. Pattern Anal Mach Intell 19(8):910–916

    Article  Google Scholar 

  22. Torres RS, Falcao A, Costa LF (2003) A graph-based approach for multiscale shape analysis. Pattern Recogn 37(6):1163–1174

    Article  Google Scholar 

  23. Plotze RO, Pqadua JG, Falvo M et al (2005) Leaf shape analysis by the multiscale Minkowski fractal dimension, a new morphometric method: a study in passiflora 1 (passifloraceae). Can J Bot Rev Can Bot 83:287–301

    Article  Google Scholar 

  24. Hu P, Wu F, Peng J et al (2016) Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution. Phys Med Biol 61(24):8676

    Article  Google Scholar 

  25. Loncaric S (1998) A survey of shape analysis techniques. Pattern Recogn 31(8):1001

    Article  Google Scholar 

  26. Wang B, Gao X, Tao D, Li X (2010) A unified tensor level set for image segmentation. IEEE Trans Syst Man Cybern Part B 40(3):857–867

    Article  Google Scholar 

  27. Gao X, Wang B, Tao D, Li X (2011) A relay level set method for automatic image segmentation. IEEE Trans Syst Man Cybern Part B 41(2):518–525

    Article  Google Scholar 

  28. Han X, Xu C, Prince J (2003) A topology preserving level set method for geometric deformable models. IEEE Trans Pattern Anal Mach Intell 25:755–768

    Article  Google Scholar 

  29. Lv Z, Tek A, Da Silva F et al (2013) Game on, science-how video game technology may help biologists tackle visualization challenges. PLoS ONE 8(3):e57990

    Article  Google Scholar 

  30. Ruan JH, Shi Y (2016) Monitoring and assessing fruit freshness in IOT-based e-commerce delivery using scenario analysis and interval number approaches. Inf Sci 373(10):557–570

    Article  Google Scholar 

  31. Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans. on Image Process 10(2):266–277

    Article  MATH  Google Scholar 

  32. Bae E, Tai XC (2009) Graph cut optimization for the piecewise constant level set method applied multiphase image segmentation. Scale Sp Var Method Comput Vis 5567:1–13

    Article  MATH  Google Scholar 

  33. Gando G, Yamada T, Sato H et al (2016) Fine-tuning deep convolutional neural networks for distinguishing illustrations from photographs. Expert Syst Appl 66:295–301

    Article  Google Scholar 

  34. Zhang C, Deng F, Zhao X et al (2016) p-th exponential synchronization of Cohen–Grossberg neural network with mixed time-varying delays and unknown parameters using impulsive control method. Neurocomputing 218:432–438

    Article  Google Scholar 

  35. Chen T, Lin L, Liu L et al (2016) DISC: deep image saliency computing via progressive representation learning. IEEE Trans Neural Netw Learn Syst 27(6):1135–1149

    Article  MathSciNet  Google Scholar 

  36. Yilmaz I, Marschalko M, Bednarik M et al (2012) Neural computing models for prediction of permeability coefficient of coarse-grained soils. Neural Comput Appl 21(5):957–968

    Article  Google Scholar 

  37. Krizhevsky A (2010). Convolutional deep belief networks on CIFAR-10. October 2010

  38. Boureau Y, Bach F, LeCun Y, Ponce J (2010) Learning mid-level features for recognition. IEEE conference on computer vision and pattern recognition

  39. Goodfellow IJ, Le QV, Saxe AM, Lee H, Ng AY (2009) Measuring invariances in deep networks. In: NIPS

  40. Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554

    Article  MathSciNet  MATH  Google Scholar 

  41. Wei W, Xu Q, Wang L et al (2014) GI/Geom/1 queue based on communication model for mesh networks. Int J Commun Syst 27(11):3013–3029

    Google Scholar 

  42. Marina E (2011) Plissiti, Hristophoros Nikou, Antonia Charchanti, Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images. Pattern Recogn Lett 23:838–853

    Google Scholar 

  43. Zamacona JR, Niehaus R, Rasin A et al (2015) Assessing diagnostic complexity: an image feature-based strategy to reduce annotation costs. Comput Biol Med 62:294–305

    Article  Google Scholar 

  44. Chen QC, Sun QS, Heng PA, Xia DS (2008) A double-threshold image binarization method based on edge detector. Pattern Recogn 41:1254–1267

    Article  Google Scholar 

  45. Thakoor N, Gao J, Jung S (2007) Hidden Makov model-based weighted likelihood discriminant for 2-D shape classifcation. IEEE Trans Image Process 161(11):2007

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Defense Engineering, PLA University of Science and Technology, Nanjing, 210007, China

    Liang Sun, Jian-chun Xing, Xun Zhang & Liang Liu

  2. College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Zhen-yu Wang

Authors
  1. Liang Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jian-chun Xing
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhen-yu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Liang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhen-yu Wang.

Ethics declarations

Conflict of interest

We declared that this manuscript was not submitted and published in the other journal, and it was only submitted to Neural Computing and Applications. There is no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, L., Xing, Jc., Wang, Zy. et al. Virtual reality of recognition technologies of the improved contour coding image based on level set and neural network models. Neural Comput & Applic 29, 1311–1330 (2018). https://doi.org/10.1007/s00521-017-2856-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-017-2856-4

Keywords

Search

Navigation