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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
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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 • Method • Experimental Results • CUDA • Q & A
Introduction(1/4) • Stereo matching • Local matching • A finite region(window size) is being computed • Global matching • Do smoothness by energy minimization techniques
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
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.
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.
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 for Correspondence Search,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 4, pp. 650-656, Apr. 2006. [8] A. Hosni, M. Bleyer, M. Gelautz, and C. Rhemann, “Local Stereo Matching Using 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 Anisotropic Diffusion,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 629-639, July 1990.
Related Work(1/6) • Adaptive-weight methods [7]
Related Work(2/6) • Adaptive-weight methods [7] • 123 • 123 • 123 Euclidean distance between the values in the CIELab color space and spatial euclidean distance truncated absolute difference (TAD)
Related Work(3/6) • Adaptive-weight methods [8]
Related Work(4/6) • Adaptive-weight methods [8] • 123 • 123 • 123 Shortest path
Related Work(5/6) • The two algorithms use the same optimization technique, winner-takes-all (WTA).
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.
Method • A : Anisotropic diffusion • B : Geodesic diffusion
Method.A(1/3) • Anisotropic diffusion
Method.A(2/3) • Anisotropic diffusion • 123 • 123 Euclidean distance between the values in the CIELab color space
Method.A(3/3) • It is aniterative computer vision technique.[9]
Method • A : Anisotropic diffusion • B : Geodesic diffusion
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.
Method.B(2/8) • Geodesic diffusion
Method.B(3/8) • Each of the four positions inherits the costs and weights of each of the four direct neighbors of each pixel.
Method.B(4/8) • Geodesic diffusion • 123 • 123 • 123
Method.B(5/8) • i= 0 right neighbors • i = 1 lower neighbors • i = 2 upper neighbors • i = 3 left neighbors
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.
Method.B(7/8) • Geodesic diffusion • 123 • 123
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.
CUDA • CUDA implementation of our algorithm ran in less than 60 milliseconds for the Tsukuba stereo pair on a GeForce 480 GTX card.