CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Logo Classification of Vehicles using SURF based on Low Detailed Feature Recognition

Published on June 2013 by T.senthilkumar, Amrita Vishwa Vidyapeetham, S.n.sivanandam, Gokul.m, Anusha.b
journal_cover_thumbnail

International Conference on Innovation in Communication, Information and Computing 2013
Foundation of Computer Science USA
ICICIC2013 - Number 3
June 2013
Authors: T.senthilkumar, Amrita Vishwa Vidyapeetham, S.n.sivanandam, Gokul.m, Anusha.b
17593311-5e7f-479f-8b69-3571f0e6089e

T.senthilkumar, Amrita Vishwa Vidyapeetham, S.n.sivanandam, Gokul.m, Anusha.b . Logo Classification of Vehicles using SURF based on Low Detailed Feature Recognition. International Conference on Innovation in Communication, Information and Computing 2013. ICICIC2013, 3 (June 2013), 5-7.

@article{
author = { T.senthilkumar, Amrita Vishwa Vidyapeetham, S.n.sivanandam, Gokul.m, Anusha.b },
title = { Logo Classification of Vehicles using SURF based on Low Detailed Feature Recognition },
journal = { International Conference on Innovation in Communication, Information and Computing 2013 },
issue_date = { June 2013 },
volume = { ICICIC2013 },
number = { 3 },
month = { June },
year = { 2013 },
issn = 0975-8887,
pages = { 5-7 },
numpages = 3,
url = { /proceedings/icicic2013/number3/12272-0153/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Innovation in Communication, Information and Computing 2013
%A T.senthilkumar
%A Amrita Vishwa Vidyapeetham
%A S.n.sivanandam
%A Gokul.m
%A Anusha.b
%T Logo Classification of Vehicles using SURF based on Low Detailed Feature Recognition
%J International Conference on Innovation in Communication, Information and Computing 2013
%@ 0975-8887
%V ICICIC2013
%N 3
%P 5-7
%D 2013
%I International Journal of Computer Applications
Abstract

In today's world a need for brand identification has become more important ,as it could be used for classification of certain brands or even in the artificial intelligence it plays an important role . This paper presents a model for car brand recognition. An input for this method is a car surveillance video obtained from the real time environment. The method used is a invariant key point Descriptor. The invariant keypoint descriptors does not change the vector value even if the image is scaled or rotated . This approach boosts the recognition accuracy compared with that of the standard SIFT based feature matching. The approach used for brand identification has been presented with results.

References
  1. C. H. Evans,"Notes on the openSURF Library",CSTR-09-001,University of Bristol,January 2009.
  2. A. Witkin "Scale-space filtering",in proc. of Artificial Intelligence,conference.
  3. Stefan Badura,Stanislav Foltan, "Advanced scale-space,invariant,low detailed feature recognition from images-car brand recognition" ,proc of International conference on Computer Science and Information Technology.
  4. Luo Juan,Oubong Gwun," A Comparison of SURF,PCA-SURF,SIFT.
  5. C. Anagnostopoulos, I. Anagnostopoulos, V. Loumos, and E. Kayafas,A license plate-recognition algorithm for intelligent transportation system applications,IEEE Trans. Intell. Transp. Syst. , no. 3, pp. 377–392,Sep. 2006.
  6. D. G. Lowe Distinctive Image Features from Scale-Invariant Keypoints. In International Journals of Computer Vision,60,2,2004,pp. 91-110
  7. J. Bauer,N. Sunderhauf,P. Protzel, Comparing Several Implementation s of Two Recently Published Feature Detectors,In proc of the International Conference on Intelligent and Autonomus Systems,IAV,Toulose,France ,2007
  8. T. Lindeberg, Scale-space theory: A basic tool for analyzing structures at different scales, J. Appl. Stat. , vol. 21, no. 1/2, pp. 224–270, 1994.
  9. H. Schulz-Mirbach, Invariant features for gray scale images, in Proc. DAGM Symp. , 1995, pp. 1–14.
  10. Apostolos P. Psyllos,Christos-Nikolaos E. Anagnostopoulos,Vehicle Logo Recognition Using SIFT-Based Enhanced Matching Scheme. IEEE Trans. Intell. Transp. Syst. , Vol. 11,no. 2,June 2010.
  11. D. Lowe, "Object recognition from local scale-invariant features," in Proc. Int. Conf. Comput. Vis. , Corfu, Greece, Sep. 1999
  12. Holland, R. J. , Hanjalic, A. , Logo recognition in video stills by string matching. In: Proceedings of the IEEE International Conference on Image Processing (ICIP 2003), Barcelona (2003)
  13. Papageorgiou, P. , Oren, M. , Poggio, T, A general framework for object detection. In: Proceedings of the International Conference on Computer Vision (ICCV 1998), Mumbai, India,(1998)
  14. H. Schulz-Mirbach, "Invariant features for gray scale images," in Prcoeedings. . DAGM Symponium. , 1995
  15. Y. Wang, Z. Liu, and F. Xiao, "A fast coarse-to-fine vehicle logo detection and recognition method," in Proceedings. IEEE International. Conference of. Robot. Biomimetics , Sanya, China, Dec. 2007.
  16. Holland, R. J. , Hanjalic, A. "Logo recognition in video stills by string matching", in Proceedings of the IEEE International Conference on Image Processing (ICIP 2003), Barcelona (2003)
  17. K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell.
Index Terms

Computer Science
Information Sciences

Keywords

Surf sift invariant Key Point hessian Matrix Feature Vector