Disparity Map Algorithm Based on Edge Preserving Filter for Stereo Video Processing
Keywords:
Bilateral Filter, Computer Vision, Edge Preserving Filter, Video Processing,Abstract
This paper proposes a new local-based stereo matching algorithm for stereo video processing. Fundamentally, the Sum of Absolute Differences (SAD) algorithm produces an accurate results on the stereo video processing for the textured regions. However, this algorithm sensitives to low texture and radiometric distortions (i.e., contrast or brightness). To overcome these problems, the proposed algorithm utilizes edgepreserving filter which is known as Bilateral Filter (BF). The BF algorithm reduces noise and sharpen the images. Additionally, BF works fine on the low or plain texture areas. The proposed algorithm produces an accurate results and performs much better compared to some established algorithms on the standard benchmarking results of the Middlebury and KITTI dataset.References
A. H. A. Hasan, R. A. Hamzah, and M. H. Johar, “Range estimation in disparity mapping for navigation of stereo vision autonomous vehicle using curve fitting tool,” IJVIPNS, vol. 9, no. 9, pp. 5–9, 2009.
R. A. Hamzah, H. Ibrahim, and A. H. A. Hassan, “Stereo matching algorithm for 3D surface reconstruction based on triangulation principle,” in Information Technology, Information Systems and Electrical Engineering (ICITISEE), International Conference on. IEEE, 2016, pp. 119–124.
I. Vedamurthy, D. C. Knill, S. J. Huang, A. Yung, J. Ding, O.-S. Kwon, D. Bavelier, and D. M. Levi, “Recovering stereo vision by squashing virtual bugs in a virtual reality environment,” Phil. Trans. R. Soc. B, vol. 371, no. 1697, pp. 1–13, 2016.
D. Scharstein, R. Szeliski, and R. Zabih, “A taxonomy and evaluation of dense two-frame stereo correspondence algorithms,” in Stereo and Multi-Baseline Vision, 2001.(SMBV 2001). Proceedings. IEEE Workshop on. IEEE, 2001, pp. 131–140.
R. A. Hamzah, H. Ibrahim, and A. H. A. Hassan, “Stereo matching algorithm based on per pixel difference adjustment, iterative guided filter and graph segmentation,” Journal of Visual Communication and Image Representation, vol. 42, pp. 145–160, 2017.
S.-S. Wu, C.-H. Tsai, and L.-G. Chen, “Efficient hardware architecture for large disparity range stereo matching based on belief propagation,” in IEEE International Workshop on Signal Processing Systems (SiPS),. IEEE, 2016, pp. 236–241.
Q. Liang, Y. Yang, and B. Liu, “Stereo matching algorithm based on ground control points using graph cut,” in 7th International Congress on Image and Signal Processing (CISP), IEEE, 2014, pp. 503–508.
H. Hirschmuller, P. R. Innocent, and J. Garibaldi, “Real-time correlation-based stereo vision with reduced border errors,” International Journal of Computer Vision, vol. 47, no. 1-3, pp. 229– 246, 2002.
J. Kowalczuk, E. T. Psota, and L. C. Perez, “Real-time stereo matching on cuda using an iterative refinement method for adaptive supportweight correspondences,” IEEE transactions on circuits and systems for video technology, vol. 23, no. 1, pp. 94–104, 2013.
Q. Yang, P. Ji, D. Li, S. Yao, and M. Zhang, “Fast stereo matching using adaptive guided filtering,” Image and Vision Computing, vol. 32, no. 3, pp. 202–211, 2014.
J. Zbontar and Y. LeCun, “Computing the stereo matching cost with a convolutional neural network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1592–1599.
W. Hu, K. Zhang, L. Sun, J. Li, Y. Li, and S. Yang, “Virtual support window for adaptive-weight stereo matching,” in Visual Communications and Image Processing (VCIP), 2011 IEEE. IEEE, 2011, pp. 1–4.
N. Einecke and J. Eggert, “Anisotropic median filtering for stereo disparity map refinement.” in VISAPP (2), 2013, pp. 189–198.
R. A. Hamzah and H. Ibrahim, “Literature survey on stereo vision disparity map algorithms,” Journal of Sensors, vol. 2016, 2015.
D. Scharstein and R. Szeliski, “Middlebury stereo evaluation - version 3 (accessed date: March 2017, http://vision.middlebury.edu/stereo/eval/references.”
A. Geiger, M. Roser, and R. Urtasun, “Efficient large-scale stereo matching,” in Asian conference on computer vision. Springer, 2010, pp. 25–38.
K. Zhang, J. Li, Y. Li, W. Hu, L. Sun, and S. Yang, “Binary stereo matching,” in Pattern Recognition (ICPR), 2012 21st International Conference on. IEEE, 2012, pp. 356–359.
G. Andreas, P. Lenz, and R. Urtasun. "Are we ready for autonomous driving? The KITTI vision benchmark suite." In 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3354-3361, 2012.
Downloads
Published
How to Cite
Issue
Section
License
TRANSFER OF COPYRIGHT AGREEMENT
The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):
- All proprietary right other than copyright, such as patent rights
- The right to make further copies of all or part of the published article for my use in classroom teaching
- The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
- The right to make copies of the published work for internal distribution within the institution that employs me
I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.
