CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Object Matching using Skeletonization based on Hamming Distance

by M. Sankari, C. Meena
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 28 - Number 7
Year of Publication: 2011
Authors: M. Sankari, C. Meena
10.5120/3396-4724

M. Sankari, C. Meena . Object Matching using Skeletonization based on Hamming Distance. International Journal of Computer Applications. 28, 7 ( August 2011), 46-50. DOI=10.5120/3396-4724

@article{ 10.5120/3396-4724,
author = { M. Sankari, C. Meena },
title = { Object Matching using Skeletonization based on Hamming Distance },
journal = { International Journal of Computer Applications },
issue_date = { August 2011 },
volume = { 28 },
number = { 7 },
month = { August },
year = { 2011 },
issn = { 0975-8887 },
pages = { 46-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume28/number7/3396-4724/ },
doi = { 10.5120/3396-4724 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:14:11.513329+05:30
%A M. Sankari
%A C. Meena
%T Object Matching using Skeletonization based on Hamming Distance
%J International Journal of Computer Applications
%@ 0975-8887
%V 28
%N 7
%P 46-50
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Identification of an object from a dynamic background is a challenging process in computer vision and pattern matching research. The proposed algorithm identifies moving objects from the sequence of video frames which contains dynamically changing backgrounds in the noisy environment. In connection with our previous work, here we have proposed a methodology to perform skeletanization of an object and identifies it. The recent vehicle recognition methods could fail to recognize an object and produce more false acceptance rate(FAR) or false rejection rate (FRR). This paper recommends a method for object identification using weighted distance to extract features. Experimental results and comparisons using real data demonstrate the pre-eminence of the proposed approach.

References
  1. M. Cristani, M. Bicegi, and V. Murino, “Integrated Region- and Pixel-based Approach to Background Modeling”, Proceedings of the MOTION, 2002.
  2. Eng, J. Wang, A. Kam, and W. Yau, “Novel Regionbased Modeling for Human Detection within High Dynamic Aquatic Environment,” Proceedings on CVPR, 2004.
  3. P. KaewTraKulPong and R. Bowden, “An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection,” In Proc. 2nd European Workshop on Advanced Video Based Surveillance Systems, 2001.
  4. Dongxiang Zhou, Hong Zhang and Nilanjan Ray, “Texture Based Background Subtraction,” Proc. IEEE Int. Conf. on Information and Automation, Zhangjiajie, China,pp. 601-605, 2008.
  5. M. Sankari, C. Meena "Adaptive Background Estimation and object detection applying in Automated Visual surveillance" International Journal of Computer science and Information Security, July 2010, Vol. 8, No. 4. pp.275-279.
  6. T. K. Dey and J. Sun, “Defining and computing curve-skeletons with medial geodesic function,” in Symp. on Geom. Proc., pp. 143–152, 2006.
  7. O. K.-C. Au, C.-L. Tai, H.-K. Chu, D. Cohen-Or, and T.-Y.Lee, “Skeleton extraction by mesh contraction,” ACM Trans. On Graph, vol. 27, no. 3, pp. 44:1–44:10, 2008.
  8. H. Sundar, D. Silver, N. Gagvani, and S. Dickinson, “Skeleton based shape matching and retrieval,” in Proc. IEEE Int. Conf. on Shape Modeling and Applications, p. 130, 2003.
  9. C.Arcelli and G.Sanniti di Baja,” A width-independent fast thinning algorithm, IEEE Trans PAMI, 7(1985) 463-474.
  10. L.Dorst, Pseudo-Euclidean skeletons, Proc. 8th Int. Conf. Pattern Recognition, Paris, France(1986) 286-288
  11. R. L. Ogniewicz. Discrete Voronoi Skeletons. Hartung-Gorre, 1993.
  12. J.M.Reinhardt and W. E. Higgins, “Efficient morphological shape representation,” IEEE Trans. Image Process., vol. 5, no. 6, pp. 89–101, Jun. 1996.
  13. P. Dimitrov, C. Phillips and K. Siddiqi , Robust and Efficient Skeletal Graphs, Proc.Computer Vision and Pattern Recognition Conf., Vol. 1, 2000, 417-423.
  14. P.E. Trahanias, “Binary shape recognition using themorphological skeleton transform”, Pattern Recognition 25(1992) 1277–1288.
  15. V. Vijaya Kumar, et. al., “A new skeletonization method based on connected component approach,” International Journal of Computer Science and Network Security, vol.8 no.2, pp.133-137, 2008.
  16. M. Sankari, C. Meena,” (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No. 6, pp.77-83, June 2011.
  17. Richard O Duda, Peter E.Hart and DavidG.Stork, “Patter Classification”,, John wiley Sons, 2001.
  18. Bremananth R, A. Chitra, “Rotation Invariant Recognition of Iris”, Journal of Systems Science & Engineering, Vol.17, No.1, pp.69-78, June 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Distance transformations false acceptance rate false rejection rate Skeletanization Traffic video sequences weighted distance

Powered by PhDFocusTM