[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

A Novel Image Mosaicking Technique for Enlarging the Field of View of Images Transmitted over Wireless Image Sensor Networks

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

Existing image mosaicking algorithms generate a complete scene that incorporates a number of images captured by several cameras. The traditional image mosaicking approaches cannot be applied directly to the emerging Wireless Image Sensor Networks (WISNs), since the low performance of image transmission over wireless sensor networks causes a noticeable delay before an entire image is received by a control center node. In this work, we propose a Progressive Image Mosaicking Algorithm (PIMA) based on the multi-scan feature of Progressive JPEG (P-JPEG). The originality of PIMA is based essentially on how it successfully performs mosaicking by using incremental image quality, as opposed to traditional methods that require complete data from all images. PIMA builds mosaics of images that are decoded from P-JPEG scans at three levels of quality, and delivers an approximate view of the scene in a short time while the reception of further image data is still in progress. Thereafter, it updates the image registration on two other refined levels to gradually enhance the display quality. We also propose the concept of Richer Information and Likeliest (RIL) block pair, which is a variation of the Sum of Absolute Difference (SAD). RIL can improve significantly the accuracy of image registration. We have conducted an extensive set of experiments and evaluated our proposed schemes against selected existing approaches. Our performance results indicate that PIMA decreases the delay before the first display of the scene, while preserving equivalent performance and image quality when compared to existing patch-based image mosaicking algorithms.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Akyildiz IF, Melodia T, Chowdhury KR (2007) A survey on wireless multimedia sensor networks. IEEE Wirel Commun 14(6):32–39

    Article  Google Scholar 

  2. Baker S, Matthews I (2004) Lucas-Kanade 20 years on: a unifying framework. Int J Comput Vis 56:221–255

    Article  Google Scholar 

  3. Bartoli A, Coquerelle M, Sturm P (2004) A framework for pencil-of-points structures-from-motion. In: Proceedings of eighth European conference on computer vision (ECCV 2004), pp 28–40

  4. Black MJ, Anandan P (1996) The robust estimation of multiple motions: parametric and piecewise-smooth flow fields. Comput Vis Image Underst 63(1):75–104

    Article  Google Scholar 

  5. Boukerche A (2008) Algorithms and protocols for wireless sensor networks. IEEE, Wiley

    Book  Google Scholar 

  6. Boukerche A, Feng J, Pazzi RW, Du Y, Huang Y (2008) Reconstructing the plenoptic function with wireless multimedia sensor networks. In: Proceedings of the 33rd IEEE conference on local computer networks (LCN 2008), pp 74–81

  7. Matas J et al (2004) Robust wide baseline stereo from maximally stable extremal regions. J Image Vis Comput 22(10):761–767

    Article  Google Scholar 

  8. Forstner W (1994) A framework for low level feature extraction. In: Proceedings of third European conference on computer vision (ECCV’94), pp 383–394

  9. Fuh C-S, Maragos P (1991) Motion displacement estimation using an affine model for image matching. Opt Eng 30(7):881–887

    Article  Google Scholar 

  10. Gennert MA (1988) Brightness-based stereo matching. In: Second international conference on computer vision, pp 139–143

  11. MSU Graphics and Media Lab. Msu video quality measurement tool. http://compression.ru/video/quality_measure/video_measurement_tool_en.html. Accessed Jan 2009

  12. Hampel FR, Ronchetti EM, Rousseeuw PJ, Stahel WA (2005) Robust statistics: the approach based on influence functions. Wiley, New York

    Google Scholar 

  13. Harris C, Stephens MJ (1988) A combined corner and edge detector. In: Proceedings Alvey vision conference, pp 147–152

  14. He Z (2007) Video compression and data flow for video surveillance. Technical report CSD-91-646, Texas Instruments

  15. Huber PJ (1981) Robust statistics. Wiley, New York

    Book  MATH  Google Scholar 

  16. Kadir T, Zisserman A, Brady M (2004) An affine invariant salient region detector. In: Proceedings of eighth European conference on computer vision (ECCV 2004), pp 228–241

  17. Lindeberg T (1990) Scale-space for discrete signals. IEEE Trans Pattern Anal Mach Intell 12(3):234–254

    Article  Google Scholar 

  18. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110

    Article  Google Scholar 

  19. Lucas BD, Kanade T (1981) An iterative image registration technique with an application in stereo vision. In: Proceedings of the 1981 DARPA image understanding workshop, pp 121–130

  20. Mikolajczyk K, Schmid C (2004) Scale and affine invariant interest point detectors. Int J Comput Vis 60(1):63–86

    Article  Google Scholar 

  21. Moravec H (1990) The Stanford cart and the CMU rover. In: Cox IJ, Wilfong GT (eds) Autonomous robot vehicles. Springer, New York, pp 407–419

    Google Scholar 

  22. Rousseeuw PJ, Leroy AM (2003) Robust regression and outlier detection. Wiley, New York

    Google Scholar 

  23. Shi J, Tomasi C (1994) Good features to track. In: Proceedings of ninth international conference on computer vision and pattern recognition (CVPR’94), pp 593–600

  24. Stewart CV (1999) Robust parameter estimation in computer vision. SIAM Rev 41:513–537

    Article  MATH  MathSciNet  Google Scholar 

  25. Szeliski R (2006) Image alignment and stitching: a tutorial. Now, Hanover

    MATH  Google Scholar 

  26. Taubman DS, Marcellin MW (2002) JPEG2000—image compression fundamentals, standards and practice. Springer, Dordrecht

    Google Scholar 

  27. Triggs B (2004) Detecting keypoints with stable position, orientation, and scale under illumination changes. In: Proceedings of eighth European conference on computer vision (ECCV 2004), pp 100–113

  28. Wallace GK (1992) The jpeg still picture compression standard. IEEE Trans Consum Electron 38(1):28–34

    Article  Google Scholar 

  29. Zoghlami I, Faugeras O, Deriche R (1997) Using geometric corners to build a 2d mosaic from a set of images. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition (CVPR’97), pp 420–425

Download references

Author information

Authors and Affiliations

  1. PARADISE Research Laboratory, University of Ottawa, Ottawa, Ontario, Canada

    Richard W. Pazzi, Azzedine Boukerche, Jing Feng & Ying Huang

Authors
  1. Richard W. Pazzi
    View author publications

    Search author on:PubMed Google Scholar

  2. Azzedine Boukerche
    View author publications

    Search author on:PubMed Google Scholar

  3. Jing Feng
    View author publications

    Search author on:PubMed Google Scholar

  4. Ying Huang
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Richard W. Pazzi.

Additional information

This research is partially sponsored by grants from NSERC, the Canada Research Chair Program, the Ontario Distinguished Researcher Award, the Ontario Research Funds (ORF), and the Ontario Centres of Excellence (OCE).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pazzi, R.W., Boukerche, A., Feng, J. et al. A Novel Image Mosaicking Technique for Enlarging the Field of View of Images Transmitted over Wireless Image Sensor Networks. Mobile Netw Appl 15, 589–606 (2010). https://doi.org/10.1007/s11036-009-0206-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-009-0206-1

Keywords