پیوندهای مفید
آمار
| تعداد نشریات | 7 |
| تعداد شماره‌ها | 399 |
| تعداد مقالات | 5,389 |
| تعداد مشاهده مقاله | 5,288,172 |
| تعداد دریافت فایل اصل مقاله | 4,882,910 |
Optimizing Disparity Candidates Space in Dense Stereo Matching | ||
| AUT Journal of Modeling and Simulation | ||
| مقاله 6، دوره 43، شماره 2، بهمن 2011، صفحه 43-51 اصل مقاله (289.04 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/miscj.2011.170 | ||
| نویسندگان | ||
| Ali M . Fotouhi؛ Abolghasem A. Raieii | ||
| چکیده | ||
| In this paper, a new approach for optimizing disparity candidates space is proposed for the solution of dense stereo matching problem. The main objectives of this approachare the reduction of average number of disparity candidates per pixel with low computational cost and high assurance of retaining the correct answer. These can be realized due to the effective use of multiple radial windows, intensity information, and some usual and new constraints, in a reasonable manner. The new space improved by the new idea validation and correction retains those candidates, which satisfy more constraints and especially being more promising to satisfy the implied assumption in using support windows, i.e. the disparity consistency of the window pixels. To evaluate the proposed space, the weighted window is used to estimate dense disparity map in this space. The experimental results on the standard stereo images indicate an overall speedup factor of 11 and the improved disparity map. | ||
| کلیدواژه‌ها | ||
| Disparity Candidate – Radial Window – Validation and Correction – Weighted Window | ||
| عنوان مقاله [English] | ||
| Optimizing Disparity Candidates Space in Dense Stereo Matching | ||
| چکیده [English] | ||
| In this paper, a new approach for optimizing disparity candidates space is proposed for the solution of dense stereo matching problem. The main objectives of this approachare the reduction of average number of disparity candidates per pixel with low computational cost and high assurance of retaining the correct answer. These can be realized due to the effective use of multiple radial windows, intensity information, and some usual and new constraints, in a reasonable manner. The new space improved by the new idea validation and correction retains those candidates, which satisfy more constraints and especially being more promising to satisfy the implied assumption in using support windows, i.e. the disparity consistency of the window pixels. To evaluate the proposed space, the weighted window is used to estimate dense disparity map in this space. The experimental results on the standard stereo images indicate an overall speedup factor of 11 and the improved disparity map. | ||
| کلیدواژه‌ها [English] | ||
| Disparity Candidate – Radial Window – Validation and Correction – Weighted Window | ||
| مراجع | ||
|
[1] Lin MH, Tomasi C, “Surfaces with occlusions from layered stereo”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 26(8), 2004. [2] Q. Yáng, L. Wang, R. Yang, H. Stewénius, and D. Nistér. “Stereo matching with color-weighted correlation, hierarchical belief propagation and occlusion handling”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31(3): 492-504, 2009. [3] Y. Taguchi, B. Wilburn, and L. Zitnick, “Stereo reconstruction with mixed pixels using adaptive over-segmentation”, In Proc. of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pages: 1-8, 2008. [4] P.H.S. Torr, A. Criminisi, “Dense stereo using pivoted dynamic programming”, Elsevier, Image and Vision Computing, Vol. 22: 795–806, 2004. [5] Kim J. Bobick, Kyoung T., Byoung T. and Sang L. “A dense stereo matching using two-pass dynamic programming with generalized ground control points”, IEEE Conf. Computer Vision and Pattern Recognition (CVPR). 19/05: 1063-69, 2005. [6] Wang, Liang; Liao, Miao; Gong, “High-Quality Real-Time Stereo Using Adaptive Cost Aggregation and Dynamic Programming”. Third International Symposium on 3D Data Processing, Visualization, and Transmission, Page(s):798 – 805, 2006. [7] Aaron F. Bobick and Stephen S. Intille. “Large occlusion stereo”, International Journal of Computer Vision, Vol . 33(3): 181-200, 1999. [8] Dinkar N.Bhat Shree K. Nayar , “Ordinal Measures for visual Correspondence”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20(4): 415-423, 1998. [9] Takeo Kanade and M. Okutomi. “A stereo matching algorithm with an adaptive window: Theory and experiment”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol .16(9):920-932, 1994. [10] Olga Veksler “stereo correspondence With Compact Windows via Minimum Ratio Cycle”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol . 24(12): 1654-1660, 2002. [11] Heiko Hirschmuller, Peter R. Innocent, and Jon Garibaldi. “Real-time correlation based stereo vision with reduced border errors”, International Journal of Computer Vision, Vol . 47: 229-246, 2002. [12] T. Darrel, “A Radial Cumulative Similarity Transform for Robust Image Correspondence”, Proc. IEEE Conf. Computer Vision and Pattern Recognition(CVPR), 656-662, 1998. [13] K.-J. Yoon and I.-S. Kweon. “Adaptive support-weight Approach for Correspondence Search”, IEEE Trans.on Pattern Analysis and Machine Intelligence, Vol. 28(4): 650-656, 2006. [14] Abhijit Ogale ,Yiannis Aloimonos. “Stereo correspondence with slanted surfaces: critical implications of horizontal slant”. IEEE Conf. Comp. Vision and Pattern Recognition: 568-573, 2004. [15] Daniel Scharstein and Richard Szeliski. “A taxonomy and evaluation of dense two-frame stereo correspondence algorithms”, International Journal of Computer Vision, Vol. 47: 7-42, 2002. [16] Geoffrey Egnal and Richard P.Wildes, “Detecting Binocular Half-Occlusions: Empirical Comparisons of Five Approaches”, IEEE Trans.on Pattern Analysis and Machine Intelligence, Vol. 24(8): 1127-1133, 2002. | ||
|
آمار تعداد مشاهده مقاله: 1,870 تعداد دریافت فایل اصل مقاله: 1,149 |
||