D. Marr and T. Poggio. Cooperative computation of stereo disparity. Science, vol.194:283--287, 1976. Home/Search Document Not in Database Summary Related Articles Check |
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A Taxonomy and Evaluation of Dense Two-Frame Stereo.. - Scharstein, Szeliski (2001) (97 citations) (Correct)
.... annealing [75, 6] probabilistic (mean field) diffusion [97] or graph cuts [23] In between these two broad classes are certain iterative algorithms that do not explicitly state a global function that is to be minimized, but whose behavior mimics closely that of iterative optimization algorithms [73, 97, 132]. Hierarchical (coarse to fine) algorithms resemble such iterative algorithms, but typically operate on an image pyramid, where results from coarser levels are used to constrain a more local search at finer levels [126, 90, 11] The most common pixel based matching costs include squared ....
....that match at this disparity. Smaller dark regions are often the result of textureless regions. Other traditional matching costs include normalized cross correlation [51, 93, 19] which behaves similar to sumof squared differences (SSD) and binary matching costs (i.e. match no match) [73], based on binary features such as edges [4, 50, 27] or the sign of the Laplacian [82] Binary matching costs are not commonly used in dense stereo methods, however. Some costs are insensitive to differences in camera gain or bias, for example gradient based measures [100, 95] and non parametric ....
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D. Marr and T. Poggio. Cooperative computation of stereo disparity. Science, 194:283--287, 1976.
A Taxonomy and Evaluation of Dense Two-Frame Stereo.. - Scharstein, Szeliski (2001) (97 citations) (Correct)
.... annealing [47, 4] probabilistic (mean field) diffusion [63] or graph cuts [18] In between these two broad classes are certain iterative algorithms that do not explicitly state a global function that is to be minimized, but whose behavior mimics closely that of iterative optimization algorithms [46, 63, 83]. Hierarchical (coarse to fine) algorithms resemble such iterative algorithms, but typically operate on an image pyramid [80, 58, 7] 3.1. Matching cost computation The most common pixel based matching costs include squared intensity differences (SSD) 34, 1, 48, 68] and absolute intensity ....
....12, 63] These measures are useful because they limit the influence of mismatches during aggregation. Other traditional matching costs include normalized cross correlation [34, 60, 15] which behaves similar to sum of squared differences (SSD) and binary matching costs (i.e. match no match) [46], based on binary features such as edges [33] or the sign of the Laplacian [51] Binary matching costs are not commonly used in dense stereo methods, however. Some costs are insensitive to differences in camera gain or bias, for example gradient based measures [61] and nonparametric measures, ....
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D. Marr and T Poggio. Cooperative computation of stereo disparity. Science, 194:283-287, 1976.
3D from Stereo Using Neural Networks - Stolle (2002) (Correct)
....in only one image. Another approach to recovering shape from vision is to cast the problem as a multiple constraint problem where adjacent image points have to be at similar depth and points with the same disparity are at the same depth. 2 First Approach Marr and Poggio presented this idea in [10]. Comparing computers to brains, they point out that although the brain s main feature is its high connectivity which makes it di#erent from computers, the same algorithm should be able to be simulated on a serial computer, although at slower speeds. The important feature to extract from the high ....
....di#erent from computers, the same algorithm should be able to be simulated on a serial computer, although at slower speeds. The important feature to extract from the high connectivity of the brain is that the computation of shape from stereo image involves many local processes that run in parallel. [10] Furthermore, if it was possible to find features in both images, they could be matched under two basic constraints: Uniqueness and Continuity. This means that a feature in one image can only be matched to at most one feature in the other image and that the resulting disparity that arises from the ....
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Marr, D., Poggio, T. (1976). "Cooperative Computation of Stereo Disparity ". Science. Volume 194: 283-287.
A Bayesian Foundation for Active Stereo Vision - Matthies, Okutomi (1989) (1 citation) (Correct)
....system aspects of this approach. The central system issue is how to find stereo correspondences efficiently and reliably. There are two types of approach: those that constrain depth estimation through heuristic assumptions about surface shape, in particular assumptions about local smoothness [4, 13, 23, 25, 27, 30], and those that obtain constraint by augmenting the sensor, in particular by using redundant images. Redundant images can come from trinocular camera systems [21, 25] fine motion image sequences [3, 19] or the use of fine motion to initialize stereo fusion [8] Redundant sensing is the more ....
D. Mart and T. Poggio. Cooperative computation of stereo disparity. Science, 194:283-287, 1976.
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D.Marr, T.Poggio, \Cooperative Computation of Stereo Disparity ". Science, New Series, Volume 194, Issue 4262 (Oct. 15, 1976), 283-287.
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D. Marr and T. Poggio. Cooperative computation of stereo disparity. Science, vol.194:283--287, 1976.
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D. Marr and T. Poggio. Cooperative computation of stereo disparity. Science, 194:283--287, 1976.
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D. Marr and T. Poggio. Cooperative computation of stereo disparity. Science, 194:283--287, 1976.
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Marr, D. and Poggio, T., Cooperative computation of stereo disparity, At Memo 364, AI Lab, MIT, Cambridge, MA, 1976. Received July 1981; revised version received July 1982
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