Advanced SearchSearch Tips
Fast Stitching Algorithm by using Feature Tracking
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
  • Journal title : Journal of Broadcast Engineering
  • Volume 20, Issue 5,  2015, pp.728-737
  • Publisher : The Korean Institute of Broadcast and Media Engineers
  • DOI : 10.5909/JBE.2015.20.5.728
 Title & Authors
Fast Stitching Algorithm by using Feature Tracking
Park, Siyoung; Kim, Jongho; Yoo, Jisang;
  PDF(new window)
Stitching algorithm obtain a descriptor of the feature points extracted from multiple images, and create a single image through the matching process between the each of the feature points. In this paper, a feature extraction and matching techniques for the creation of a high-speed panorama using video input is proposed. Features from Accelerated Segment Test(FAST) is used for the feature extraction at high speed. A new feature point matching process, different from the conventional method is proposed. In the matching process, by tracking region containing the feature point through the Mean shift vector required for matching is obtained. Obtained vector is used to match the extracted feature points. In order to remove the outlier, the RANdom Sample Consensus(RANSAC) method is used. By obtaining a homography transformation matrix of the two input images, a single panoramic image is generated. Through experimental results, we show that the proposed algorithm improve of speed panoramic image generation compared to than the existing method.
Panorama;Stitching;FAST;Mean shift;
 Cited by
Y. J. Cho, J. M. Seok, S. Y. Lim, S. W. An, J. I Seo, and J. H. Chan, “Post-UHD Realistic media, high quality panoramic AV technology”, Electronics and Telecommunications Trends, vol. 20, no. 3, pp. 33-46, June 2014.

Y. J. Lee, Y. J. Joe, M. S. Ki, S. Y. Lim, H. G. Lee and J. H. Cha, “High quality human fusion type panorama services”, The Korean Institute of Communications and Information Sciences, vol. 28, no. 6, pp. 11-20, 2011, 5.

D. Lowe, “Distinctive image features from scale-invariant keypoints”, International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, November 2004. crossref(new window)

H. Bay, A. Ess, T. Tuytelaars, L. V. Gool, “Speeded-up robust feature”, Computer Vision and Image Understanding, vol. 10, no. 3, pp. 346-359, June 2008. crossref(new window)

L. M. J. Florack, B. M. Ter Haar Romeny, J. J. Koenderink, M. A. Viergever, “General intensity transformations and differential invariants”, Journal of Mathematical Imaging and Vision, Vol. 4, no. 2, pp. 171-187, May 1994. crossref(new window)

E. Rosten and T. Drummond, “Machine learning for high-speed corner detection”, European Conference on Computer Vision, Graz, Austria, pp. 430-443, May 2006.

D. Comaniciu, V. Ramesh and P. Meer, “Real-time tracking of non-rigid objects using mean shift”, Proc. 2000 IEEE Conference Computer Vision and Patter Recognition, vol. 2, pp. 142-149, June 2000.

M. A. Fischler and R. C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography”, Communications of the ACM, vol. 24, no. 6, pp. 381-395, June 1981. crossref(new window)

E. Dubrofsky, “Homography estimation”, UNIVERSITY OF BRITISH COLUMBIA, March 2009.

C. Harris and M. Stephens, “A combined corner and edge detector”, proceedings of the 4th Alvey Vision Conference, pp. 147-151, August 1988.

S. M. Smith and J. M. Brady, “Susan – a new approach to low level image processing“, International Journal of Computer Vision, vol. 23, no. 1, pp. 45-78, May 1997. crossref(new window)

J. Tompkin, Optical flow an introduction, University College London((UCL) Computer Science Dept., machine Vision –Practical 2, March 2008.

D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis”, IEEE Computer Society, vol. 24, no. 5, pp. 603-619, May 2002.