Computer Vision – ECCV 2010: 11th European Conference on by Andrej Mikulík, Michal Perdoch, Ondřej Chum, Jiří Matas

By Andrej Mikulík, Michal Perdoch, Ondřej Chum, Jiří Matas (auth.), Kostas Daniilidis, Petros Maragos, Nikos Paragios (eds.)

The 2010 variation of the ecu convention on desktop imaginative and prescient was once held in Heraklion, Crete. the decision for papers attracted an absolute checklist of 1,174 submissions. We describe the following the choice of the permitted papers: Thirty-eight region chairs have been chosen coming from Europe (18), united states and Canada (16), and Asia (4). Their choice was once in line with the next standards: (1) Researchers who had served a minimum of twice as sector Chairs in the earlier years at significant imaginative and prescient meetings have been excluded; (2) Researchers who served as zone Chairs on the 2010 machine imaginative and prescient and trend attractiveness have been additionally excluded (exception: ECCV 2012 software Chairs); (3) Minimization of overlap brought by means of region Chairs being former scholar and advisors; (4) 20% of the world Chairs had by no means served prior to in a massive convention; (5) the realm Chair choice approach made all attainable efforts to accomplish a cheap geographic distribution among international locations, thematic components and traits in computing device imaginative and prescient. every one zone Chair was once assigned by means of this system Chairs among 28–32 papers. in accordance with paper content material, the world Chair advised as much as seven power reviewers in line with paper. Such task used to be made utilizing all reviewers within the database together with the conflicting ones. this system Chairs manually entered the lacking clash domain names of roughly three hundred reviewers. in keeping with the advice of the world Chairs, 3 reviewers have been chosen in keeping with paper (with no less than one being of the head 3 suggestions), with 99.

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Extra resources for Computer Vision – ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part III

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To derive the FS-PSF for arbitrary motions, we simply need to derive t(x). 1 Constant Velocity For constant velocity motion at νc pixels/chop, we assume the first exposed pixel is the 0-th pixel, and we have x(t) = νc · t and t(x) = x/νc . The FS-PSF is thus: p0 (x) = S( νxc ) S(t(x)) = , x = 1, · · · , νc Ms νc νc (4) Since the last exposed pixel coordinate has x(Ms ) = νc Ms , we can compute the normalized FS-PSF as: p(x) = p0 (x) νc Ms x=1 p0 (x) = S( νxc ) νc Es , x = 1, · · · , νc Ms (5) Eq.

Ding, S. McCloskey, and J. Yu Fig. 8. Motion-aware Fluttered Shutter. (a) is the PSF and the MTF under constant velocity. 8pixels/chop) using the same sequence as in (a). (d) is the PSF/MTF using our motion-aware sequence. (e) and (f) are synthetically blurred images of (b) using acceleration PSFs in (c) and (d). (g) and (h) are the corresponding deblurred images, (i) and (j) show the close-up views. 6 Conclusion and Limitations We have presented a new fluttered-shutter-based motion estimation and deblurring framework.

Our work is also related to Depth-from-Defocus (DfD) methods based on the coded apertures [21]. Although both DfD and motion estimation can be formulated as kernel estimation problems, motion blur kernels are usually complex yet spatially-invariant whereas defocus blur kernels are simple but spatially variant. , via image statistics [3] while DfD techniques rely on other types of priors such as smoothness or edges [22,23]. In this paper we analyze image statistics under the fluttered shutter for motion estimation.

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