Noise-driven Concurrent Stereo Matching (NCSM)

Noise-driven Concurrent Stereo Matching

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Footnodes

... network ^2.1: In application to a much simpler problem of binary labelling where such approach leads to an exact solution, the maximum-flow / minimum-cut technique was proposed first in [44].
... seen ^2.2: Scene assumptions enable the matching regions to be delimited. Assuming no ``out-of-image" matching eliminates the lower right triangle. Restricting a closest distance (or maximum disparity) eliminates much of the upper left. However, plenty of candidate matches remain!
... ^3.1: To generate that noise, the normal centred deviation $\Delta$ with distribution $N(0, 1)^{2}$ is produced from a uniformly distributed random deviation and converted to the normal deviation with distribution $N(\mu,\sigma)$ using an obvious relationship $\mu + \sigma\Delta$ .
... NCSM-SDPS)^5.1: NCSM-SDPS has complexity because SDPS is linear in the image width ( $\sqrt{n}$ ) and repeated at each row ( $\sqrt{n}$ ) for image size ; the complexity of NCSM-ITER is , where is the range of disparities and is the number of iterations.