Skip to main content

Histogram of Oriented Gradients for Image Mosaicing

  • Chapter
  • First Online:
Innovations in Computational Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 713))

  • 532 Accesses

Abstract

Image Stitching can be defined as a process of combining multiple input images or some of their features into a single image without introducing distortion or loss of information. The aim of stitching is to integrate all the imperative information from multiple source images to create a fused mosaic. Therefore, the newly generated image should contain a full description of the scenery than any of its individual source images and is more suitable for human visual and machine perception. With the recent rapid developments in the domain of imaging technologies, mosaicing has become a reality in wide fields such as remote sensing, medical imaging, machine vision, and the military applications. Image Stitching therefore provides an effective way by reducing huge volume of information by extracting only the useful information from the source images and blending them into a high-resolution image called mosaic.

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

Access this chapter

eBook
USD 16.99
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
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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. L.G. Brown, A survey of image registration techniques. Comput. Surv. 24(4), 325–376 (1992)

    Article  Google Scholar 

  2. B.Z.B. Zhang, Computer vision vs. human vision, Cogn. Informatics (ICCI), in 2010 9th IEEE International Conference (2004), p. 318108

    Google Scholar 

  3. Survey (ebtsam, elmogy)

    Google Scholar 

  4. CV_Overview

    Google Scholar 

  5. C. Guo, X. Li, L. Zhong, X. Luo, A fast and accurate corner detector based on Harris algorithm, in 3rd International Symposium Intelligence Information Technology Applied IITA 2009, vol. 2, no. 4, pp. 49–52 (2009)

    Google Scholar 

  6. L. Zeng, S. Zhang, J. Zhang, Y. Zhang, Dynamic image mosaic via SIFT and dynamic programming. Mach. Vis. Appl. 25(5), 1271–1282 (2014)

    Article  Google Scholar 

  7. J. Melorose, R. Perroy, S. Careas, No Title No Title. Statew. Agric. L. Use Baseline 1, 2015 (2015)

    Google Scholar 

  8. Z. Wang, F. Yan, Y. Zheng, An adaptive uniform distribution surf for image stitching, in 2013 6th International Congress Image Signal Processes, vol. 2, (2013), no. Cisp, pp. 888–892

    Google Scholar 

  9. E. Rosten, T. Drummond, Machine learning for high speed corner detection, Computer Vision—ECCV 2006, (2004), pp. 430–443

    Google Scholar 

  10. N. Dalal, W. Triggs, Histograms of oriented gradients for human detection, in 2005 IEEE Computer. Social Conferences Computer Vision Pattern Recognition CVPR05, vol. 1, (2004), no. 3, pp. 886–893

    Google Scholar 

  11. A. Kulkarni, J. Jagtap, V. Harpale, Object recognition with ORB and its implementation on FPGA, vol. 3 (The accents Organization, 2013)

    Google Scholar 

  12. R. Szeliski, Video mosaics for virtual environments. IEEE Comput. Graph. Appl. 16(2), 22–30 (1996)

    Article  Google Scholar 

  13. I.S. Sevcenco, P.J. Hampton, P. Agathoklis, Seamless stitching of images based on a HAAR wavelet 2D integration method. Department of Electrical and Computer Engineering University of Victoria (2011)

    Google Scholar 

  14. P. Pérez, M. Gangnet, A. Blake, Poisson image editing. ACM Trans. Graph. 22(3), 313 (2003)

    Article  Google Scholar 

  15. J. Jia, J. Sun, C.-K. Tang, H.-Y. Shum, Drag-and-drop pasting. Siggraph 25(3), 631 (2006)

    Article  Google Scholar 

  16. A. Levin, A. Zomet, S. Peleg, Y. Weiss, Seamless image stitching in the gradient domain. Eccv 3024, 377–389 (2004)

    MATH  Google Scholar 

  17. M. Brown, D.G. Lowe, Recognising panoramas, in Proceeding IEEE International Conference Computer Vision, (2003) pp. 1218–1225

    Google Scholar 

  18. B. Triggs, P. Mclauchlan, R. Hartley, A. Fitzgibbon, Bundle adjustment—a modern synthesis 1 introduction. Vis. Algorithms Theory Pract. 34099, 1–71 (2000)

    Google Scholar 

  19. M. Zhu, W. Wang, B. Liu, J. Huang, A fast image stitching algorithm via multiple-constraint corner matching. Math. Prob. Eng. 2013 (2013)

    Google Scholar 

  20. P. Singh, S. Batra, H.R. Sharma, Steganographic methods based on digital logic, in Proceedings of the 6th WSEAS International Conference on Signal Processing, Dallas, Texas, USA (2007), pp. 157–162

    Google Scholar 

  21. A.V. Krishna, Performance evaluation of new encryption algorithms with emphasis on probabilistic encryption and time stamp in network security, vol. 3, no. 5, pp. 39–46 (2009)

    Google Scholar 

  22. D. Hooda, P. Singh, A comprehensive survey of video encryption algorithms. IJCA 59(1), 14–19 (2012)

    Article  Google Scholar 

  23. R. Singh, P. Singh, M. Duhan, An effective implementation of security based algorithmic approach in mobile adhoc networks. Hum—centric Comput. Inf. Sci. 4(1), 7:1–7:14 (2014)

    Google Scholar 

  24. D. Singh, D. Sethi, Reduction of noise from speech signal using HAAR and biorthogonal wavelet. 7109, pp. 263–269 (2011)

    Google Scholar 

  25. G. Tsai, Histogram of oriented gradients. Lecture Series University of Michigan (2010), pp. 1–6

    Google Scholar 

  26. H.-Y. Shum, R. Szeliski, Construction of panoramic mosaics with global and local alignment. Int. J. Comput. Vis. 36(2), 101–130 (2000)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ridhi Arora .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Arora, R., Singh, P. (2018). Histogram of Oriented Gradients for Image Mosaicing. In: Panda, B., Sharma, S., Batra, U. (eds) Innovations in Computational Intelligence . Studies in Computational Intelligence, vol 713. Springer, Singapore. https://doi.org/10.1007/978-981-10-4555-4_14

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-4555-4_14

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-4554-7

  • Online ISBN: 978-981-10-4555-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics