By Gaurav Sharma, Sibt ul Hussain, Frédéric Jurie (auth.), Andrew Fitzgibbon, Svetlana Lazebnik, Pietro Perona, Yoichi Sato, Cordelia Schmid (eds.)

The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed complaints of the twelfth eu convention on desktop imaginative and prescient, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers awarded 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 type, visible positive aspects and photograph matching, visible tracking: motion and actions, versions, optimisation, studying, visible monitoring and picture registration, photometry: lighting fixtures and color, and snapshot segmentation.

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The hill-climbing approach uses a lot more iterations than previous methods, but each iteration is done extremely fast. This enables stopping the algorithm at any given time, because the time to finish the current iteration is negligible. 6 Experiments We report results on the Berkeley Segmentation Dataset (BSD) [7], using the standard metrics to evaluate superpixels, as used in most recent superpixel papers [6,12,13,15,17]. The BSD consists of 500 images split into 200 training, 100 validation and 200 test images.

U∗N . 3 Measuring the Deblurring Performance To compare similarity between two images a and b (represented as vectors), we first estimate the optimal scaling α ˆ and translation Tˆ such that the L2 norm ˆ , Tˆ = minα,T a − T (αb) 2 . e. α calculate the peak-signal-to-noise ratio (PSNR) as PSNR(a, b) = 10 log10 m2 ai − Tˆ (ˆ α bi ) 2 (4) i with . e. m = 255 as we work with 8bit encoding. Given a sequence of ground truth images u∗1 , . . , u∗N along the trajectory, we define the PSNR similarity between an estimated image uˆ and the ground truth as the maximum PSNR between u ˆ and any of the images along the trajectory, ˆ).

I (8) k If the patch Ni contains a unique superpixel, G(s) is at its maximum. Observe that it is not possible that such maximum is achieved in all pixels, because the patches near the boundaries contain multiple superpixel labelings. However, penalizing patches containing several superpixel labelings reduces the amount of pixels close to a boundary, and thus enforces regular shapes. Furthermore, in the case that a boundary yields a shape which is not smooth, the amount of patches that take multiple superpixel labels is higher.

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