Abstract
Accurate estimation of disparity is one of the most active research area in computer vision. In the last few decades numerous algorithms have been invented to find disparity precisely. However, these inventions throws problem in selecting most appropriate one for the required application. A detailed analysis is mandatory to solve this kind of problem. The main objective of this paper is to empirically evaluate a set of well known correlation based stereo matching algorithms. A qualitative and quantitative analysis results will be useful for selecting the most appropriate algorithm for the given application. The presented analysis is mainly focused on the evaluation of errors, robustness to change in illumination and the computation cost required for each algorithm.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Scharstein, D., Szeliski, R., Zabih, R.: A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms. Int. Journal of Computer Vision 47, 7–42 (2002)
Aschwanden, P., Guggenbuhl, W.: Experimental Results From A Comparative Study On Correlation Type Registration Algorithms. In: Förstner, W., Ruwiedel, S. (eds.) Robust Computer Vision, Wichmann, pp. 268–282 (1992)
Hseu, H., Bhalerao, A., Wilson, R.: Image Matching Based on the Co-Occurrence Matrix. Technical Report. University of Warwick, Coventry, UK (1998)
Faugeras, O., et al.: Qualitative And Quantitative Comparison of Some Area and Feature Based Stereo Algorithms. In: Förstner, W., Ruwiedel, S. (eds.) Robust Computer Vision, Wichmann, pp. 1–26 (1992)
Arsenio, A., Marques, J.S.: Performance Analysis and Characterization of Matching Algorithms. In: Proc. of the 5th International Symposium on Intelligent Robotic Systems, Stockholm, Sweden (1997)
Sun, C.: Multi-Resolution Rectangular Sub Regioning Stereo Matching Using Fast Correlation And Dynamic Programming Techniques. CMIS Report No 98/246 (1998)
Hirschmuller, H., Innocent, P.R., Garibaldi, J.: Real-Time Correlation-Based Stereo Vision With Reduced Border Errors. Int. Journal of Computer Vision 47, 229–246 (2002)
Okutomi, M., Katayama, Y., Oka, S.: A Simple Stereo Algorithm To Recover Precise Object Boundaries and Smooth Surfaces. Int. Journal of Computer Vision 47, 261–273 (2002)
Yoon, K., Kweon, I.: Adaptive support-weight approach for correspondence search. IEEE Transactions on PAMI 28(4), 650–656 (2006)
Zhang, K., Lu, J., Lafruit, G.: Cross-based local stereo matching using orthogonal integral images. IEEE Transaction on CSVT 19(7), 1073–1079 (2009)
Middlebury Stereo Vision, http://www.vision.middlebury.edu/stereo/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
U., R., Makkithaya, K., A.K., K. (2012). Qualitative and Quantitative Evaluation of Correlation Based Stereo Matching Algorithms. In: Thilagam, P.S., Pais, A.R., Chandrasekaran, K., Balakrishnan, N. (eds) Advanced Computing, Networking and Security. ADCONS 2011. Lecture Notes in Computer Science, vol 7135. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29280-4_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-29280-4_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29279-8
Online ISBN: 978-3-642-29280-4
eBook Packages: Computer ScienceComputer Science (R0)