Skip to main content
SpringerLink
Log in

Object Proposal Refinement Based on Contour Support for Augmented Reality

  • Conference paper
  • First Online:
E-Learning and Games (Edutainment 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9654))

  • 1579 Accesses

Abstract

Object detection and segmentation are indispensable for image scene understanding in augmented reality games. Object proposals delineate candidate objects in the image, and are widely used to speed up object searching in object detection and segmentation. This paper presents an approach for reducing the redundancy in object proposals. We compute contour support of object proposals, and construct contour support constraints using the characteristics of contour support distributions for foreground objects and image background. According to the constructed constraints, we propose the accepting and rejecting strategies to refine object proposals. Experiments demonstrate that our method reduces redundant object proposals and improves proposal accuracy for low proposal budgets.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Alexe, B., Deselaers, T., Ferrari, V.: Measuring the objectness of image windows. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2189–2202 (2012)

    Article  Google Scholar 

  2. Carreira, J., Li, F., Sminchisescu, C.: Object recognition by sequential figure-ground ranking. Int. J. Comput. Vis. 98(3), 243–262 (2012)

    Article  MathSciNet  Google Scholar 

  3. Carreira, J., Sminchisescu, C.: CPMC: automatic object segmentation using constrained parametric min-cuts. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1312–1328 (2012)

    Article  Google Scholar 

  4. Cheng, M.M., Zhang, Z., Lin, W.Y., Torr, P.: Bing: binarized normed gradients for objectness estimation at 300fps. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3286–3293 (2014)

    Google Scholar 

  5. Dollár, P., Zitnick, C.L.: Structured forests for fast edge detection. In: IEEE International Conference on Computer Vision, pp. 1841–1848 (2013)

    Google Scholar 

  6. Estrada, F.J., Jepson, A.D.: Robust boundary detectionwith adaptive grouping. In: IEEE Conference on Computer Vision and Pattern Recognition Workshop, p. 184 (2006)

    Google Scholar 

  7. Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The Pascal visual object classes (voc) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)

    Article  Google Scholar 

  8. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)

    Google Scholar 

  9. Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: Simultaneous detection and segmentation. In: European Conference on Computer Vision, pp. 297–312 (2014)

    Google Scholar 

  10. Hosang, J., Benenson, R., Schiele, B.: How good are detection proposals, really? In: British Machine Vision Conference, pp. 1–12 (2014)

    Google Scholar 

  11. Krahenbuhl, P., Koltun, V.: Learning to propose objects. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1574–1582 (2015)

    Google Scholar 

  12. Krähenbühl, P., Koltun, V.: Geodesic object proposals. In: European Conference on Computer Vision, pp. 725–739 (2014)

    Google Scholar 

  13. Levinshtein, A., Sminchisescu, C., Dickinson, S.: Optimal image and video closure by superpixel grouping. Int. J. Comput. Vis. 100(1), 99–119 (2012)

    Article  Google Scholar 

  14. Lu, C., Liu, S., Jia, J., Tang, C.K.: Contour box: rejecting object proposals without explicit closed contours. In: IEEE International Conference on Computer Vision, pp. 2021–2029 (2015)

    Google Scholar 

  15. Van de Sande, K.E., Uijlings, J.R., Gevers, T., Smeulders, A.W.: Segmentation as selective search for object recognition. In: IEEE International Conference on Computer Vision, pp. 1879–1886 (2011)

    Google Scholar 

  16. Wang, S., Kubota, T., Siskind, J.M., Wang, J.: Salient closed boundary extraction with ratio contour. IEEE Trans. Pattern Anal. Mach. Intell. 27(4), 546–561 (2005)

    Article  Google Scholar 

  17. Wang, X., Yang, M., Zhu, S., Lin, Y.: Regionlets for generic object detection. In: IEEE International Conference on Computer Vision, pp. 17–24 (2013)

    Google Scholar 

  18. Zitnick, C.L., Dollár, P.: Edge boxes: locating object proposals from edges. In: European Conference on Computer Vision, pp. 391–405 (2014)

    Google Scholar 

Download references

Acknowledgements

This work is supported by National Natural Science Foundation of China under Grant No. 91520301.

Author information

Authors and Affiliations

  1. Xi’an Jiaotong University, Xi’an, Shaanxi, 710049, People’s Republic of China

    Xiao Huang, Yuanqi Su & Yuehu Liu

Authors
  1. Xiao Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuanqi Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuehu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiao Huang .

Editor information

Editors and Affiliations

  1. Computer Science, John Moores University, Liverpool, United Kingdom

    Abdennour El Rhalibi

  2. Science and Technology, Bournemouth University, Bournemouth, United Kingdom

    Feng Tian

  3. Hangzhou Normal University, Hangzhou, Zhejiang, China

    Zhigeng Pan

  4. Computer Science and Technology, University of Bedfordshire, Luton, United Kingdom

    Baoquan Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Huang, X., Su, Y., Liu, Y. (2016). Object Proposal Refinement Based on Contour Support for Augmented Reality. In: El Rhalibi, A., Tian, F., Pan, Z., Liu, B. (eds) E-Learning and Games. Edutainment 2016. Lecture Notes in Computer Science(), vol 9654. Springer, Cham. https://doi.org/10.1007/978-3-319-40259-8_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-40259-8_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40258-1

  • Online ISBN: 978-3-319-40259-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation