CFP last date
20 May 2024
Reseach Article

Object Recognition Technique based on Level Set Method and Neural Network

by V. N. Pawar, S. N. Talbar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 40 - Number 2
Year of Publication: 2012
Authors: V. N. Pawar, S. N. Talbar
10.5120/4926-7153

V. N. Pawar, S. N. Talbar . Object Recognition Technique based on Level Set Method and Neural Network. International Journal of Computer Applications. 40, 2 ( February 2012), 8-12. DOI=10.5120/4926-7153

@article{ 10.5120/4926-7153,
author = { V. N. Pawar, S. N. Talbar },
title = { Object Recognition Technique based on Level Set Method and Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 40 },
number = { 2 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 8-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume40/number2/4926-7153/ },
doi = { 10.5120/4926-7153 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:27:00.112260+05:30
%A V. N. Pawar
%A S. N. Talbar
%T Object Recognition Technique based on Level Set Method and Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 40
%N 2
%P 8-12
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Object recognition is the task of finding and labeling parts of a two-dimensional (2D) image of a scene that correspond to objects in the scene. In this paper, we have proposed an efficient approach using level set method for extracting object shape contour and convex hull as a shape invariant features to the Feed forward Neural Network classifier for object recognition. We extracted the shape contour by level set method. Then, we have obtained invariant shape feature, convex hull of the objects. This convex hull set serves as a pattern for the Neural Network. Initially Feed forward neural network trained on the odd data set and tested on even data set. Our approach is evaluated on the Amsterdam Library of Object Images collection, a large collection of object images containing 1000 objects recorded under various imaging circumstances. The experimental results clearly demonstrate that our approach significantly outperforms. The proposed method is shown to be effective under a wide variety of imaging conditions.

References
  1. P. Duygulu, K. Barnard, N. de Freitas, and D. Forsyth, Object Recognition as Machine Translation: Learning a lexicon for a fixed image vocabulary, In European Conference on Computer Vision (ECCV) Copenhagen, 2002
  2. D. A. Forsyth and J. Ponce, Computer Vision: a modern approach, Prentice Hall,2002
  3. A. Hoogs, R. Collins, R. Kaucic, and J. Mundy, A common set of perceptual observables for grouping, figure - ground discrimination, and texture classification, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 4, pp. 458 ,474, 2003.
  4. B. Ko and H. Byun, Extracting Salient Regions And Learning Importance Scores In Region-Based Image Retrieval, International Journal of Patter Recognition and Artificial Intelligence, Vol. 17, No. 8, pp. 1349 ,1367, 2003.
  5. R. C. Nelson, Memory-based recognition for 3-d objects, In ARPA Image Understanding Workshop, pp: 1305 ,1310, February 1996.
  6. C. Schmid and R. Mohr,Local gray value invariants for image retrieval, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.19, Issue.5, 1997, pp: 530,535.
  7. R.P.N. Rao and D.H. Ballard,Natural basis functions and topographic memory for face recognition, In International Joint Conference on Artificial Intelligence, pp: 10,17, 1995.
  8. S. Osher and J. Sethian, Fronts propagating with curvature dependent speed: Algorithms based on hamilton-jacobi formulation. J. Comput. Phys., vol. 79, pp. 12, 49, 1988.
  9. Johan Lie Variational Image Segmentation using Discontinuous Level Set Formulations,Cand. Scient. Thesis in Applied Mathematics,University of Bergen, December 12th 2003
  10. Thomas H. Cormen Introduction to Algorithms,Second edition,MIT Press, 2001
  11. Werbos, P. J., Beyond regression: New tools for prediction and analysis in the behavioral sciences, Ph.D. Thesis, Harvard University,Cambridge, MA.1974
  12. Rumelhart, D. E., G. E. Hinton, and R. J.Williams, Learning internal representations by error propagation, in D. E. Rumelhart and J. L. McCleland, eds. (Cambridge, MA: MIT Press), vol. 1, Chapter 8.1986
  13. J. M. Geusebroek, G. J. Burghouts, and A. W. M. Smeulders,The Amsterdam library of object images, Int. J. Comput. Vision, Vol. 61, No. 1, pp. 103,112, January, 2005.
  14. Amsterdam Library of Object Images (ALOI) Datasets from http://staff.science.uva.nl/ aloi/
Index Terms

Computer Science
Information Sciences

Keywords

Object Recognition Shape feature Convex Hull Feed Forward Neural Network