Projects – Ψηφιακή Επεξεργασία Εικόνας και Βίντεο Χ. Θεοχαράτος – Δεκ. 2015

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PROJECT TITLES

1. Kernel Fusion for Better Image Deblurring Long Mai, Feng Liu, “Kernel Fusion for Better Image Deblurring”, IEEE CVPR 2015, Boston, MA, June 2015. http://web.cecs.pdx.edu/~fliu/project/kernelfusion/ Includes the PDF version of the CVPR paper, dataset and basic code (in the paper).

2. Intra-Frame Deblurring by Leveraging Inter-Frame Camera Motion H. Zhang, J. Yang, Intra-Frame Deblurring by Leveraging Inter-Frame Camera Motion” IEEE CVPR 2015, Boston, MA, June 2015. http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zhang_Intra-Frame_Deblurring_by_2015_CVPR_paper.pdf https://sites.google.com/site/hczhang1/home Includes the PDF version of the CVPR paper and basic code.

3. Motion magnification C. Liu, A. Torralba, W.T. Freeman, F. Durand and E.H. Adelson, “Motion Magnification”, SIGGRAPH2005, Los Angeles.

http://people.csail.mit.edu/celiu/motionmag/motionmag.html

Includes the PDF version of the SIGGRAPH paper, PDF slides presentation, videos from the presentation, and Matlab source code.

Projects – Ψηφιακή Επεξεργασία Εικόνας και Βίντεο Χ. Θεοχαράτος – Δεκ. 2015

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3. Defocus magnification S. Bae, F. Durand, “Defocus Magnification”, EUROGRAPHICS 2007 / D. Cohen-Or and P. Slavík (Guest Editors), Volume 26 (2007), Number 3.

http://people.csail.mit.edu/soonmin/dof/dof.pdf, Matlab code available.

5. Motion frame interpolation This is the technique of generating intermediate images from moving images of e.g. 15 frames/sec to make them 30/60 frames/sec at maximum. The conventional frame interpolation technique is not really able to handle the rapidly-switched scenes and other scenes which require very large movement between frames. It sometimes caused an unnatural interpolation frame. The target is to detect the movement within the images and create the intermediate images corresponding to the more smooth and natural movements.

http://image.diku.dk/larslau/papers/isvc2012.pdf

http://www.icita.org/2014/CD/papers/kr-JGKim.pdf

http://research.microsoft.com/pubs/69174/lowcomplexitymc.pdf

Projects – Ψηφιακή Επεξεργασία Εικόνας και Βίντεο Χ. Θεοχαράτος – Δεκ. 2015

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6. Learning to Detect Motion Boundaries P. Weinzaepfel, J. Revaud, Z. Harchaoui, C. Schmid, “Learning to Detect Motion Boundaries”, IEEE CVPR 2015, Boston, MA, June 2015.

http://lear.inrialpes.fr/research/motionboundaries/

Includes the PDF version of the CVPR paper, YouTube Motion Boundaries (YMB) dataset and Motion boundaries detection code.

7. Reflection Removal using Ghosting Cues YiChang Shih, Dilip Krishnan, Fredo Durand, William T. Freeman, “Reflection Removal using Ghosting Cues”, IEEE CVPR 2015, Boston, MA, June 2015.

https://dilipkay.wordpress.com/reflection_ghosting/

https://dilipkay.files.wordpress.com/2015/04/reflection5.pdf

Includes the PDF version of the CVPR paper, data and code.

Projects – Ψηφιακή Επεξεργασία Εικόνας και Βίντεο Χ. Θεοχαράτος – Δεκ. 2015

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8. Learning to Segment Moving Objects in Videos K. Fragkiadaki, P. Arbelaez, P. Felsen and J. Malik, “Learning to Segment Moving Objects in Videos”, IEEE CVPR 2015, Boston, MA, June 2015.

http://www.cs.berkeley.edu/~katef/videolearn.html

Includes the PDF version of the CVPR paper and code.

9. Grouping-Based Low-Rank Video Completion and 3D Reconstruction K. Fragkiadaki, M. Salas, P. Arbelaez, and J. Malik, “Grouping-Based Low-Rank Video Completion and 3D Reconstruction”, NIPS 2014.

http://www.cs.berkeley.edu/~katef/nrsfm.html

Projects – Ψηφιακή Επεξεργασία Εικόνας και Βίντεο Χ. Θεοχαράτος – Δεκ. 2015

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Includes the PDF version of the NIPS paper and code.

10. Face Video Retrieval with Image Query Yan Li, Ruiping Wang, Zhiwu Huang, Shiguang Shan, Xilin Chen, “Face Video Retrieval with Image Query via Hashing across Euclidean Space and Riemannian Manifold”, IEEE CVPR 2015, Boston, MA, June 2015.

http://stoudemireyan32.wix.com/yanli#!publication/c1cax

http://www.vision.ee.ethz.ch/~zzhiwu/

11. Viewfinder Alignment Andrew Adams, Natasha Gelfand, and Kari Pulli, “Viewfinder Alignment”, EUROGRAPHICS 2008.

http://graphics.stanford.edu/papers/viewfinderalignment/

Projects – Ψηφιακή Επεξεργασία Εικόνας και Βίντεο Χ. Θεοχαράτος – Δεκ. 2015

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12. Facial Attractiveness and Relative Ranking Automatic evaluation of human facial attractiveness is a challenging problem that has received little attention from the computer vision community. Here, we approach beauty from a relative ranking perspective.

http://vision.ucsd.edu/project/facial-attractiveness-and-relative-ranking

13. Sharpening Out of Focus Images Michael W. Tao, Jitendra Malik, and Ravi Ramamoorthi, “Sharpening Out of Focus Images using High-Frequency Transfer”, EUROGRAPHICS 2013.

http://www.cs.berkeley.edu/~ravir/EUROGRAPHICS_SUPP/index.html

http://www.cs.berkeley.edu/~ravir/reconstruct_EG.pdf

14. Object Matching and Panorama Stitching http://www.cs.cornell.edu/courses/cs4670/2010fa/projects/p2b/

Contains description, dataset and code (in C++).

Projects – Ψηφιακή Επεξεργασία Εικόνας και Βίντεο Χ. Θεοχαράτος – Δεκ. 2015

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15. Color Temperature Color temperature is a characteristic of visible light that has important applications in lighting, photography, videography, publishing, manufacturing, astrophysics, horticulture, and other fields. The color temperature of a light source is the temperature of an ideal black body radiator that radiates light of comparable hue to that of the light source. What is it, How to Alter?

http://en.wikipedia.org/wiki/Color_temperature

16. Action Recognition in Videos using Motion Decomposition Shandong Wu, Omar Oreifej, and Mubarak Shah, “Action Recognition in Videos Acquired by a Moving Camera Using Motion Decomposition of Lagrangian Particle Trajectories”, International Conference on Computer Vision, November 2011, Barcelona, Spain.

http://crcv.ucf.edu/projects/MotionDecomposition/

Includes the PDF version of the ICCV paper, examples, presentation and code.

Projects – Ψηφιακή Επεξεργασία Εικόνας και Βίντεο Χ. Θεοχαράτος – Δεκ. 2015

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Χρήστος Θεοχαράτος

htheohar@upatras.gr