Feature Extraction of Real-Time Image Using SIFT Algorithm



Abstract Views
1740

Downloads
1586

Citations

Share

##plugins.themes.bootstrap3.article.sidebar##

Submitted Apr 1, 2020
Published May 21, 2020
10.24018/ejece.2020.4.3.206

Abstract viewed = 1740 times

Table of Contents

##plugins.themes.bootstrap3.article.main##

This paper deals with image processing and feature extraction. Feature extraction plays a vital role in the field of image processing. There exist different image pre-processing approaches for feature extraction such as binarization, thresholding, resizing, normalisation so on...Then after these techniques are applied to obtain high clarity images. In Feature extraction object recognition and stereo matching are at the base of many computer vision problems. The descriptor generator module is changed for increasing the performance of algorithm. SIFT algorithm consist of two modules such as key point detection module and descriptor generation module. When compared to recent solution, the descriptor generation module speed is fifteen times faster and the time for feature extraction is also reduced.

Keywords: SIFT (Scale invariant feature transform), SIFT HOG (Scale invariant feature transform histogram of oriented gradients), SURF (Speeded up robust features).

Downloads

Download data is not yet available.

References

  1. J.S. Patil, Dr.G. Pradeepini "An examination on restorative photograph evaluation the usage of photograph Descriptors frameworks." overall mag of pc applications (IJCA). ICNIC 2016.
     Google Scholar
  2. Dalal, N. Additionally, Triggs, B., "Histograms of orientated Gradients for Human Detection," IEEE pc Society appear on pc vision and test commonness, 2005, San Diego, CA, u.S. Of the us.
     Google Scholar
  3. D. G. Lowe, "object affirmation from close-by scale invariant aptitudes," in IEEE ICCV, 1999, vol. 2, p. 1150.
     Google Scholar
  4. N. Inoue and K. Shinoda, ?Fast coding of feature vectors using neighborto-neighbor search,? IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 6, pp. 1160?1184, 2016.
     Google Scholar
  5. J. Y. Choi, K. N. Plataniotis, and Y. M. Ro, ?Using color local binary pattern features for face recognition,? in Proc. 17th IEEE Int. Conf. Image Process. (ICIP), Sep. 2010, pp. 4541?4544.
     Google Scholar
  6. David G Lowe, ?Distinctive Image Features from Scale-Invariant Keypoints?, International Journal of Computer Vision, vol.50, No. 2, 2004, pp.91-110
     Google Scholar
  7. Herbert Bay, Tinne Tuytelaars and Luc Van Gool. ?Speeded-up robust features (SURF).? Computer vision and image understanding, vol.110, No.3, 2008, pp.346-359.
     Google Scholar
  8. Ethan Rublee, Vincent Rabaud, Kurt Konolige and Gary Bradski, ?ORB: and efficient alternative to SIFT or SURF?, IEEE International Conference on Computer Vision,2011.
     Google Scholar
  9. E. Karami, S. Prasad, and M. Shehata, ?Image Matching Using SIFT, SURF, BRIEF, and ORB: Performance Comparison for Distorted Images,? in Proceedings of the 2015 Newfoundland Electrical and Computer Engineering Conference, St. John?s, Canada, November,2015.
     Google Scholar
  10. Y. Bastanlar, A. Temizel and Y. Yard?mc?, ?Improved SIFT matching for image pairs with scale difference?, Electron. Lett., vol. 46, no. 5, p. 346, 2010.
     Google Scholar
  11. Y. Cong, X. Chen and Y. Li, ?Research on the SIFT Algorithm in Image Matching,? AMM, vol. 121-126, pp.4656-4660,2011.
     Google Scholar
  12. B. Liao and H. Wang, ?The Optimization of SIFT Feature Matching Algorithm on Face Recognition Based on BP Neural Network,? AMM, vol. 743, pp. 359-364,2015.
     Google Scholar
  13. X. Qiao, ?Research on the Algorithm of Image Matching Based on Improved SIFT,? AMM, vol. 686, pp. 348-353,2014.
     Google Scholar
  14. F. Tian and Y. Yan, ?A SIFT Feature Matching Algorithm Based on Semi-Variance Function,? AMR, vol. 647, pp. 896-900,2013.
     Google Scholar
  15. H. Zhang and L. Cao, ?An Image Matching Algorithm Based on SUSAN-SIFT Algorithm,? AMM, vol. 325-326, pp. 1588-1592,2013.
     Google Scholar
  16. D. Nister and H. Stewenius, ?Scalable recognition with a vocabulary tree,? Proc. International Conference on Computer Vision and Pattern Recognition,Vol. 2, pp.2161-2168, 2006.
     Google Scholar