Computer Vision – ECCV 2012: 12th European Conference on by Dhruv Batra, Payman Yadollahpour, Abner Guzman-Rivera,
By Dhruv Batra, Payman Yadollahpour, Abner Guzman-Rivera, Gregory Shakhnarovich (auth.), Andrew Fitzgibbon, Svetlana Lazebnik, Pietro Perona, Yoichi Sato, Cordelia Schmid (eds.)
The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed lawsuits of the twelfth eu convention on computing device imaginative and prescient, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers offered have been conscientiously reviewed and chosen from 1437 submissions. The papers are prepared in topical sections on geometry, 2nd and 3D shapes, 3D reconstruction, visible popularity and category, visible beneficial properties and photo matching, visible tracking: motion and actions, versions, optimisation, studying, visible monitoring and photograph registration, photometry: lights and color, and snapshot segmentation.
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Extra info for Computer Vision – ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part V
It should be noted that algorithms which output near optimal results take couple of order of magnitude more time. MPI is only two to three times slower than GC but has very poor energy output. MPI seems to move into a local minima fast and does have the ability to move out of it. Performance of AMPI on the potentials used was very similar to MPI in both energy outputted and time taken. Also, algorithms like DD and ICM which have good energy convergence started taking tens of minutes at image size of 150 × 150 and simply failed to run when image size was increased to 200 × 200.
Consider the operation on dual variables in which ball pa decreases its height by reducing variable Vc1 ,p,a by δ without aﬀecting the relative ordering of balls in well p. To continue satisfying the constraint given in equation (9) will require increase in the value(s) of other V variables associated with clique c1 . One possibility is to increase variable Vc1 ,q,a of q a by the same amount. In eﬀect the height of pa decreases and that of q a increases. We may view this change in heights of pa and q a as a consequence of sending δ ﬂow from pa to q a in clique c1 .
This is typically treated as a ﬁgure/ground segmentation task, with the MAP solution presented to the user. The user 10 D. Batra et al. Table 1. Interactive segmentation: pixel accuracies averaged over 50 test images MAP Div MBest-dot prod. 17 then provides additional supervision, leading to updated MAP solutions, till the MAP solution is acceptable. In order to minimize user interactions, the interface could show not a single cutout, but a set of possible cutouts for the user to simply select from.