Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Visual Computing

ISVC 2016: Advances in Visual Computing pp 298–308Cite as

Object Detection Based on Image Blur Using Spatial-Domain Filtering with Haar-Like Features

  • Conference paper
  • First Online:

  • 4159 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10072))

Abstract

In general, out-of-focus blur is considered to be disturbance that reduces the detection accuracy for object detection, and many researchers have tried to remove such noise. The authors proposed an object detection scheme that exploits information included in image blur. This scheme showed good accuracy for object detection, but it has a critical problem: huge computational cost is required owing to the DFT needed to evaluate image blur. This paper proposes a novel object detection scheme using the difference in image blur evaluated with simple spatial-domain filtering. Experimental results using synthetic images show that the scheme achieves perfect classification, though our previous scheme has about a 2.40% miss rate at 0.1 FPPI for circle detection. In addition to the improvement in accuracy, the processing speed becomes about 431 times faster than that of the old scheme.

This is a preview of subscription content, 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   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Learn about institutional subscriptions

References

  1. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition, vol. 1, pp. 511–518 (2001)

    Google Scholar 

  2. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition, vol. 1, pp. 886–893 (2005)

    Google Scholar 

  3. Hiromoto, M., Sugano, H., Miyamoto, R.: Partially parallel architecture for AdaBoost-based detection with haar-like features. IEEE Trans. Circuits Syst. Video Technol. 19, 41–52 (2009)

    Article  Google Scholar 

  4. Hiromoto, M., Miyamoto, R.: Hardware architecture for high-accuracy real-time pedestrian detection with CoHOG features. In: Proceedings of IEEE International Conference on Computer Vision Workshops, pp. 894–899 (2009)

    Google Scholar 

  5. Yu, J., Miyamoto, R., Onoye, T.: Fast pedestrian detection using a soft-cascade of the CoHOG-based classifier: how to speed-up SVM classifiers based on multiple-instance pruning. IEEE Trans. Image Process. 22, 4752–4761 (2013)

    Article  MathSciNet  Google Scholar 

  6. Miyamoto, R., Oki, T.: Soccer player detection with only color features selected using informed haar-like features. In: Blanc-Talon, J., Distante, C., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2016. LNCS, vol. 10016, pp. 238–249. Springer, Heidelberg (2016). doi:10.1007/978-3-319-48680-2_22

    Chapter  Google Scholar 

  7. Miyamoto, R., Jaehoon, Y., Onoye, T.: Normalized channel features for accurate pedestrian detection. In: Proceedings of IEEE ISCCSP, pp. 582–585 (2014)

    Google Scholar 

  8. Schmidhuber, J.: Multi-column deep neural networks for image classification. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition, pp. 3642–3649 (2012)

    Google Scholar 

  9. Erhan, D., Szegedy, C., Toshev, A., Anguelov, D.: Scalable object detection using deep neural networks. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition, pp. 2155–2162 (2014)

    Google Scholar 

  10. Yang, Y., Shu, G., Shah, M.: Semi-supervised learning of feature hierarchies for object detection in a video. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition, pp. 1650–1657 (2013)

    Google Scholar 

  11. Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88, 303–338 (2010)

    Article  Google Scholar 

  12. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: Proceedings IEEE Conference on Computer Vision Pattern Recognition, pp. 248–255 (2009)

    Google Scholar 

  13. Zhang, S., Benenson, R., Schiele, B.: Filtered channel features for pedestrian detection. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition, pp. 1751–1760 (2015)

    Google Scholar 

  14. Miyamoto, R., Kobayashi, S.: Object detection based on image blur evaluated by discrete Fourier transform and Haar-like features. IEICE Trans. Fundam. E99-A(11), 1990–1999 (2016)

    Google Scholar 

  15. Rhemann, C., Rother, C., Kohli, P., Gelautz, M.: A spatially varying PSF-based prior for alpha matting. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition, pp. 2149–2156 (2010)

    Google Scholar 

  16. Couzinie-Devy, F., Sun, J., Alahari, K., Ponce, J.: Learning to estimate and remove non-uniform image blur. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition, pp. 1075–1082 (2013)

    Google Scholar 

  17. Flusser, J., Suk, T., Boldys, J., Zitová, B.: Projection operators and moment invariants to image blurring. IEEE Trans. Pattern Anal. Mach. Intell. 37, 786–802 (2015)

    Article  Google Scholar 

  18. Zhang, S., Bauckhage, C., Cremers, A.: Informed haar-like features improve pedestrian detection. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition, pp. 947–954 (2014)

    Google Scholar 

  19. Zhang, C., Viola, P.A.: Multiple-instance pruning for learning efficient cascade detectors. In: Platt, J.C., Koller, D., Singer, Y., Roweis, S.T., (eds.) Advances in Neural Information Processing Systems 20. Curran Associates, Inc., pp. 1681–1688 (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, School of Science and Technology, Meiji University, Kawasaki, Kanagawa, 214-8571, Japan

    Ryusuke Miyamoto

  2. Department of Fundamental Science and Technology, Graduate School of Science and Technology, Meiji University, Kawasaki, Kanagawa, 214-8571, Japan

    Shingo Kobayashi

Authors
  1. Ryusuke Miyamoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shingo Kobayashi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ryusuke Miyamoto .

Editor information

Editors and Affiliations

  1. University of Nevada , Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center , Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory , Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute , Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University , O'Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs , Redwood City, California, USA

    Sandra Skaff

  7. University of Florida , Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc. , Mountain View, California, USA

    Jianyuan Min

  9. Osaka University , Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute , Monterey, California, USA

    Amela Sadagic

  11. University of Arizona , Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud , Orsay, France

    Tobias Isenberg

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Miyamoto, R., Kobayashi, S. (2016). Object Detection Based on Image Blur Using Spatial-Domain Filtering with Haar-Like Features. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-50835-1_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50834-4

  • Online ISBN: 978-3-319-50835-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation