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Paper in ICCP 2013 “Post-processing approach for radiometric self-calibration of video”

April 19th, 2013 Irfan Essa Posted in Computational Photography and Video, ICCP, Matthias Grundmann, Papers, Sing Bing Kang No Comments »

  • M. Grundmann, C. McClanahan, S. B. Kang, and I. Essa (2013), “Post-processing Approach for Radiometric Self-Calibration of Video,” in Proceedings of IEEE International Conference on Computational Photography (ICCP), 2013. [PDF] [WEBSITE] [VIDEO] [DOI] [BIBTEX]
    @inproceedings{2013-Grundmann-PARSV,
      Author = {Matthias Grundmann and Chris McClanahan and Sing Bing Kang and Irfan Essa},
      Booktitle = {{Proceedings of IEEE International Conference on Computational Photography (ICCP)}},
      Date-Added = {2013-06-25 11:54:57 +0000},
      Date-Modified = {2014-04-28 17:09:49 +0000},
      Doi = {10.1109/ICCPhot.2013.6528307},
      Month = {April},
      Organization = {IEEE Computer Society},
      Pdf = {http://www.cc.gatech.edu/~irfan/p/2013-Grundmann-PARSV.pdf},
      Title = {Post-processing Approach for Radiometric Self-Calibration of Video},
      Url = {http://www.cc.gatech.edu/cpl/projects/radiometric},
      Video = {http://www.youtube.com/watch?v=sC942ZB4WuM},
      Year = {2013},
      Bdsk-Url-1 = {http://www.cc.gatech.edu/cpl/projects/radiometric},
      Bdsk-Url-2 = {http://dx.doi.org/10.1109/ICCPhot.2013.6528307}}

Abstract

We present a novel data-driven technique for radiometric self-calibration of video from an unknown camera. Our approach self-calibrates radiometric variations in video, and is applied as a post-process; there is no need to access the camera, and in particular it is applicable to internet videos. This technique builds on empirical evidence that in video the camera response function (CRF) should be regarded time variant, as it changes with scene content and exposure, instead of relying on a single camera response function. We show that a time-varying mixture of responses produces better accuracy and consistently reduces the error in mapping intensity to irradiance when compared to a single response model. Furthermore, our mixture model counteracts the effects of possible nonlinear exposure-dependent intensity perturbations and white-balance changes caused by proprietary camera firmware. We further show how radiometrically calibrated video improves the performance of other video analysis algorithms, enabling a video segmentation algorithm to be invariant to exposure and gain variations over the sequence. We validate our data-driven technique on videos from a variety of cameras and demonstrate the generality of our approach by applying it to internet video.

via IEEE Xplore – Post-processing approach for radiometric self-calibration of video.

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Paper in IEEE ICCP 2012: “Calibration-Free Rolling Shutter Removal”

April 28th, 2012 Irfan Essa Posted in Computational Photography and Video, Daniel Castro, ICCP, Matthias Grundmann, Vivek Kwatra No Comments »

Calibration-Free Rolling Shutter Removal

  • M. Grundmann, V. Kwatra, D. Castro, and I. Essa (2012), “Calibration-Free Rolling Shutter Removal,” in Proceedings of IEEE Conference on Computational Photography (ICCP), 2012. [PDF] [WEBSITE] [VIDEO] [DOI] [BLOG] [BIBTEX]
    @inproceedings{2012-Grundmann-CRSR,
      Author = {Matthias Grundmann and Vivek Kwatra and Daniel Castro and Irfan Essa},
      Blog = {http://prof.irfanessa.com/2012/04/28/paper-iccp12/},
      Booktitle = {Proceedings of IEEE Conference on Computational Photography (ICCP)},
      Date-Added = {2012-04-09 22:40:38 +0000},
      Date-Modified = {2013-10-22 13:54:12 +0000},
      Doi = {10.1109/ICCPhot.2012.6215213},
      Pdf = {http://www.cc.gatech.edu/~irfan/p/2012-Grundmann-CRSR.pdf},
      Publisher = {IEEE Computer Society},
      Title = {Calibration-Free Rolling Shutter Removal},
      Url = {http://www.cc.gatech.edu/cpl/projects/rollingshutter/},
      Video = {http://www.youtube.com/watch?v=_Pr_fpbAok8},
      Year = {2012},
      Bdsk-Url-1 = {http://www.cc.gatech.edu/cpl/projects/rollingshutter/},
      Bdsk-Url-2 = {http://dx.doi.org/10.1109/ICCPhot.2012.6215213}}

Abstract

We present a novel algorithm for efficient removal of rolling shutter distortions in uncalibrated streaming videos. Our proposed method is calibration free as it does not need any knowledge of the camera used, nor does it require calibration using specially recorded calibration sequences. Our algorithm can perform rolling shutter removal under varying focal lengths, as in videos from CMOS cameras equipped with an optical zoom. We evaluate our approach across a broad range of cameras and video sequences demonstrating robustness, scalability, and repeatability. We also conducted a user study, which demonstrates a preference for the output of our algorithm over other state-of-the art methods. Our algorithm is computationally efficient, easy to parallelize, and robust to challenging artifacts introduced by various cameras with differing technologies.

Presented at IEEE International Conference on Computational Photography, Seattle, WA, April 27-29, 2012.

Winner of BEST PAPER AWARD.

 

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