Article

Comparison of Feature Detection and Matching Approaches: SIFT and SURF

Author(s):

Darshana Mistry, EInfochips Training and Research Academy; Asim Banerjee ,Dhirubhai Ambani Institute of Information and Communication Technology

Keywords

SIFT (Scale Invariant Feature Transform), SURF (Speeded Up Robust Feature), invariant, integral image, box filter

Abstract

Feature detection and matching are used in image registration, object tracking, object retrieval etc. There are number of approaches used to detect and matching of features as SIFT(Scale Invariant Feature Transform), SURF(Speeded Up Robust Feature), FAST, ORB etc. SIFT and SURF are most useful approaches to detect and matching of features because of it is invariant to scale, rotate, translation, illumination, and blur. In this paper, there is comparison between SIFT and SURF approaches are discussed. SURF is better than SIFT in rotation invariant, blur and warp transform. SIFT is better than SURF in different scale images. SURF is 3 times faster than SIFT because using of integral image and box filter. SIFT and SURF are good in illumination changes images.

Reference

    [1] David L.”Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision 60(2): 91-110, 2006. [2] Herbert B., Andreas E., Tinne T. and Luc Van G.: Speeded up Robust Feature (SURF), Journal of Computer vision and image understanding 110 (3): 346-359, 2008. [3] Herner. B., Tinee T., and Luc Van G.: “SURF: Speeded Up Robust Features”, Computer Vision- ECCV: 404-417, 2008. [4] Luo J. and Oubong G.: A Comparison of SIFT, PCA-SIFT, and SURF. International Journal of Image Processing (IJIP) 3(4): 143-152, 2009. [5] “Difference between SIFT and SURF”, https://www.quora.com/Image-Processing/Difference-between-SURF-and-SIFT-where-and-when-to-use-this-algo.23/11/2015. [6] Utsav S., Darshana M. and Asim B.: Image Registration of Multi-View Satellite Images Using Best Feature Points Detection and Matching Methods from SURF, SIFT and PCA-SIFT 1(1): 8-18, 2014. [7] HKHUST video data, “https://www.youtube.com/watch=OOUOPnLbjkI”.

Other Details

Paper ID: GRDJEV02I040013
Published in: Volume : 2, Issue : 4
Publication Date: 2017-04-01
Page(s): 7 - 13

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