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Reseach Article

Investigating the performance of Correspondence Algorithms in Vision based Driver-assistance in Indoor Environment

by F. Mahmood, Syed M. B. Haider, F. Kunwar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 60 - Number 9
Year of Publication: 2012
Authors: F. Mahmood, Syed M. B. Haider, F. Kunwar
10.5120/9718-3663

F. Mahmood, Syed M. B. Haider, F. Kunwar . Investigating the performance of Correspondence Algorithms in Vision based Driver-assistance in Indoor Environment. International Journal of Computer Applications. 60, 9 ( December 2012), 6-12. DOI=10.5120/9718-3663

@article{ 10.5120/9718-3663,
author = { F. Mahmood, Syed M. B. Haider, F. Kunwar },
title = { Investigating the performance of Correspondence Algorithms in Vision based Driver-assistance in Indoor Environment },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 60 },
number = { 9 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 6-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume60/number9/9718-3663/ },
doi = { 10.5120/9718-3663 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:06:05.073818+05:30
%A F. Mahmood
%A Syed M. B. Haider
%A F. Kunwar
%T Investigating the performance of Correspondence Algorithms in Vision based Driver-assistance in Indoor Environment
%J International Journal of Computer Applications
%@ 0975-8887
%V 60
%N 9
%P 6-12
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents the experimental comparison of fourteen stereo matching algorithms in variant illumination conditions. Different adaptations of global and local stereo matching techniques are chosen for evaluation The variant strength and weakness of the chosen correspondence algorithms are explored by employing the methodology of the prediction error strategy. The algorithms are gauged on the basis of their performance on real world data set taken in various indoor lighting conditions and at different times of the day.

References
  1. Middlebury correspondence evaluation. http://vision. middlebury. edu/correspondence
  2. R. Szeliski, R. Zabih, D. Scharstein, O. Veksler, V. Kolmogorov, A. Agarwala, M. Tappen, and C. Rother, , "A comparative study of energy minimization methods for markov random fields with smoothness-based priors," IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol. 30, no. 6, pp. 1068–1080, 2007.
  3. R. Mohan, G. Medioni, and R. Nevatia, "Correspondence error detection, correction, and evaluation," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 11, no. 2, pp. 113–120, Feb. 1989
  4. Konstantin Schauwecker, Sandino Morales, Simon Hermann, and Reinhard Klette. A Comparative Study of Correspondence-Matching Algorithms for Road-Modeling in the Presence of Windscreen Wipers in Intelligent Vehicle Symposium 2011
  5. S. Morales, T. Vaudrey, and R. Klette, "Robustness Evaluation of Correspondence Algorithms on Long Correspondence Sequences", in Proc. IEEE Intelligent Vehicles, pages 347–352, 2009
  6. P. Handschack and R. Klette, "Quantitative comparisons of differential methods for measuring of image velocity," in Proc. Aspects Visual Form Process. , Capri, Italy, 1994, pp. 241–250
  7. N. A. Thacker, A. F. Clark, J. L. Barronc, J. R. Beveridged, P. Courtneye, W. R. Crum, V. Ramesh, and C. Clark, "Performance characterization in computer vision: A guide to best practices," Comput. Vis. Image Under-stand. , vol. 109, no. 3, pp. 305–334, Mar. 2008.
  8. D. Scharstein and R. Szeliski, "A taxonomy and evaluation of dense two-frame correspondence correspondence correspondence algorithms," International Journal of Computer Vision, vol. 47, no. 1, pp. 7–42, 2002
  9. R. Mohan, G. Medioni, and R. Nevatia, "Stereo error detection, correc-tion, and evaluation," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 11, no. 2, pp. 113–120, Feb. 1989.
  10. www. middlebury. edu/stereo
  11. S. Morales and R. Klette, "A third eye for performance evaluation in stereo sequence analysis", In Proc. CAIP, LNCS 5702, pages 1078-1086, 2009
  12. Stefano Mattoccia, Simone Giardino, and Andrea Gambini. Accurate and Efficient Cost Aggregation Strategy for Stereo Stereo correspondence Based on Approximated Joint Bilateral Filtering in Asian Conference on Computer Visio(ACCV2009), September 23- 27, 2009, Xi'an, China
  13. Jan Cech and Radim Sara. Efficient Sampling of Disparity Space for Fast And Accurate Matching in CVPR 2007
  14. Emmanouil Z. Psarakis and Georgios D. Evangelidis. An Enhanced Correlation-Based Method for Stereo Stereo correspondence with Sub-Pixel Accuracy in (ICCV 2005)
  15. Shape and the stereo stereo correspondence problem, A. S. Ogale and Y. Aloimonos, In-ternational Journal of Computer Vision, vol. 65, no. 3, 147-162, Dec 2005.
  16. Pedro F. Felzenszwalb and Daniel P. Huttenlocher. Efficient Belief Propagation for Early Vision in Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'04)
  17. Vladimir Kolmogorov , Ramin Zabih "Computing Visual Stereo correspondence with occlusions via Graph Cuts"
  18. Andreas Geiger, Martin Roser and Raquel Urtasun. Efficient Large-Scale Stereo Matching in ACCV 2010
  19. B. Baykant Alagoz. Obtaining Depth Maps From Color Images By Region Based Stereo Matching Algorithms in CVPR 2008
  20. Andreas Klaus, Mario Sormann and Konrad Karner. Segment-Based Stereo Matching Using Belief Propagation and a Self-Adapting Dissimilarity Measure in18th International Conference on Pattern Recognition (ICPR'06)
  21. R. Szeliski, "Prediction error as a quality metric for motion and stereo," in Proc. ICCV, 1999, vol. 2, pp. 781–788.
  22. Reinhard Klette, Norbert Krüger, Tobi Vaudrey, Karl Pauwels, Marc van Hulle, Sandino Morales,Farid I. Kandil, Ralf Haeusler, Nicolas Pugeault, Clemens Rabe, and Markus Lapp, "Performance of Correspondence Algorithms in Vision-Based Driver Assistance Using an Online Image Sequence Database", IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 60, NO. 5, JUNE 2011
Index Terms

Computer Science
Information Sciences

Keywords

Performance Indoor Lighting Correspondence Algorithms