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SURF and RANSAC: A Conglomerative Approach to Object Recognition

by Silica Kole, Charvi Agarwal, Tripti Gupta, Sanya Singh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 109 - Number 4
Year of Publication: 2015
Authors: Silica Kole, Charvi Agarwal, Tripti Gupta, Sanya Singh
10.5120/19174-0645

Silica Kole, Charvi Agarwal, Tripti Gupta, Sanya Singh . SURF and RANSAC: A Conglomerative Approach to Object Recognition. International Journal of Computer Applications. 109, 4 ( January 2015), 7-9. DOI=10.5120/19174-0645

@article{ 10.5120/19174-0645,
author = { Silica Kole, Charvi Agarwal, Tripti Gupta, Sanya Singh },
title = { SURF and RANSAC: A Conglomerative Approach to Object Recognition },
journal = { International Journal of Computer Applications },
issue_date = { January 2015 },
volume = { 109 },
number = { 4 },
month = { January },
year = { 2015 },
issn = { 0975-8887 },
pages = { 7-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume109/number4/19174-0645/ },
doi = { 10.5120/19174-0645 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:44:24.414508+05:30
%A Silica Kole
%A Charvi Agarwal
%A Tripti Gupta
%A Sanya Singh
%T SURF and RANSAC: A Conglomerative Approach to Object Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 109
%N 4
%P 7-9
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, an object recognition system [7] has been developed that uses SURF(Speeded-Up Robust Features) and RANSAC(Random Sample Consensus) algorithms to identify a series of real-life objects in a given scene using their 2-D images. SURF algorithm has been used for feature detection, extraction and matching. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and af?ne or 3D projection[2]. RANSAC algorithm has been used to filter out the results obtained by the SURF algorithm and remove the outliers. Ten different objects have been successfully recognized using this system.

References
  1. Bay, Herbert, Tinne Tuytelaars, and Luc Van Gool. "Surf: Speeded up robust features. " Computer Vision–ECCV 2006. Springer Berlin Heidelberg, 2006. 404-417.
  2. Lowe, David G. "Object recognition from local scale-invariant features. "Computer vision, 1999. The proceedings of the seventh IEEE international conference on. Vol. 2. Ieee, 1999.
  3. Fischler, Martin A. , and Robert C. Bolles. "Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. " Communications of the ACM 24. 6 (1981): 381-395.
  4. Lowe, David G. "Three-dimensional object recognition from single two-dimensional images. " Artificial intelligence 31. 3 (1987): 355-395.
  5. Panchal, P. M. , S. R. Panchal, and S. K. Shah. "A Comparison of SIFT and SURF. " International Journal of Innovative Research in Computer and Communication Engineering 1. 2320 (2013): 9798.
  6. Bay, Herbert, et al. "Speeded-up robust features (SURF). " Computer vision and image understanding 110. 3 (2008): 346-359.
  7. Alexander Andreopoulos, John K. Tsotsos "50 Years of Object Recognition: Directions Forward", ?Department of Computer Science and Engineering,Centre for Vision Research, New York University, Toronto
Index Terms

Computer Science
Information Sciences

Keywords

SURF RANSAC object recognition computer vision

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