Near Real-Time Stereo Matching Using Geodesic Diffusion
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NEAR REAL-TIME STEREO MATCHING USING GEODESIC DIFFUSION IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 34, NO. 2, FEBRUARY 2012 1 Leonardo De-Maeztu, Arantxa Villanueva, Member, IEEE, and Rafael Cabeza Guan-Yu Liu
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Near Real-Time Stereo Matching Using Geodesic Diffusion. Leonardo De- Maeztu , Arantxa Villanueva, Member, IEEE, and Rafael Cabeza. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 34, NO. 2, FEBRUARY 2012. Guan-Yu Liu. Outline. Introduction Overview Related work - PowerPoint PPT Presentation
Transcript of Near Real-Time Stereo Matching Using Geodesic Diffusion
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NEAR REAL-TIME STEREO MATCHING USING GEODESIC DIFFUSION
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 34, NO. 2, FEBRUARY
2012
Leonardo De-Maeztu,Arantxa Villanueva, Member, IEEE, and
Rafael Cabeza
Guan-Yu Liu
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Outline Introduction Overview Related work Method Experimental Results CUDA Q & A
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Introduction(1/4)
Stereo matching Local matching
A finite region(window size) is being computed Global matching
Do smoothness by energy minimization techniques
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Introduction(2/4)
When using local support regions, it is implicitly assumed that all pixels in the region are of the same depth. the fronto-parallel surfaces assumption Adaptive-weight methods
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Introduction(3/4)
Adaptive-weight methods are the local algorithms yielding the best results. Highly time-consuming task
Anisotropic diffusion, a computer vision technique very similar to adaptive weighting but computationally less expensive. a computer vision technique very similar to adaptive
weighting but computationally less expensive.
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Introduction(4/4)
Geodesic diffusion is inspired by anisotropic diffusion. diffusing both matching costs and weights.
Near real-time execution is demonstrated using a commercial graphics card.
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Related Work
Adaptive-weight methods [7] Adaptive-weight methods [8] Anisotropic diffusion [9]
[7] K.-J. Yoon and I.S. Kweon, “Adaptive Support-Weight Approach forCorrespondence Search,” IEEE Trans. Pattern Analysis and MachineIntelligence, vol. 28, no. 4, pp. 650-656, Apr. 2006.
[8] A. Hosni, M. Bleyer, M. Gelautz, and C. Rhemann, “Local Stereo MatchingUsing Geodesic Support Weights,” Proc. Int’l Conf. Image Processing,pp. 2093-2096, 2009.
[9] P. Perona and J. Malik, “Scale-Space and Edge Detection Using AnisotropicDiffusion,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12,no. 7, pp. 629-639, July 1990.
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Related Work(1/6)
Adaptive-weight methods [7]
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Related Work(2/6)
Adaptive-weight methods [7]
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123 truncated absolute difference (TAD)
Euclidean distance between the values in the CIELab color space and spatial euclidean distance
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Related Work(3/6)
Adaptive-weight methods [8]
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Related Work(4/6)
Adaptive-weight methods [8]
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Shortest path
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Related Work(5/6)
The two algorithms use the same optimization technique, winner-takes-all (WTA).
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Related Work(6/6)
Anisotropic diffusion is a computer vision technique similar to bilateral filtering. only the comparison of each pixel with its immediate
neighbors is necessary.
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Method
A : Anisotropic diffusion
B : Geodesic diffusion
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Method.A(1/3)
Anisotropic diffusion
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Method.A(2/3)
Anisotropic diffusion
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123Euclidean distance between the values in the CIELab color space
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Method.A(3/3)
It is an iterative computer vision technique.[9]
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Method
A : Anisotropic diffusion
B : Geodesic diffusion
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Method.B(1/8)
Three principles Costs and weights are diffused so that the importance of
each cost value is known in each iteration. In each iteration, the costs and weights at each pixel are
accumulated. After the last iteration, all the support region information has been accumulated at each pixel.
To increase the efficiency of information diffusion and to avoid loops, turns in the direction of diffusion are penalized.
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Method.B(2/8)
Geodesic diffusion
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Method.B(3/8)
Each of the four positions inherits the costs and weights of each of the four direct neighbors of each pixel.
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Method.B(4/8)
Geodesic diffusion 123
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Method.B(5/8)
i = 0 right neighbors i = 1 lower neighbors i = 2 upper neighbors i = 3 left neighbors
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Method.B(6/8)
The cost and weight information derived from a direct neighbor is not returned to this neighbor.
Costs are only propagated with their full weights in the same direction of their propagation direction in the previous iteration.
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Method.B(7/8)
Geodesic diffusion 123
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Method.B(8/8)
At the end of the diffusion process, the DSI costs are normalized.
Thus, concluded, and the disparity map is then computed by selecting the lower cost disparity for each pixel WTA.
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Experimental Results(1/8)
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Experimental Results(2/8)
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Experimental Results(3/8)
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Experimental Results(4/8)
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Experimental Results(5/)
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Experimental Results(6/8)
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Experimental Results(7/8)
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Experimental Results(8/8)
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CUDA
CUDA implementation of our algorithm ran in less than 60 milliseconds for the Tsukuba stereo pair on a GeForce 480 GTX card.
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Q & A
