Eitan Sharon

I am currently at

CTO and Co-founder @ Videosurf.com             

A Computer-Vision based Search Engine for Videos

Email me here

 

   Until recently I belonged to the faculty at

   the Department of Electrical Engineering

   In the Technion - Israel Institute of Technology

 

Research interests - computer vision and math


I'm interested in the host of fundamental problems in computer vision leading up to object recognition.

 

Current research: collaborating with David Mumford on the study of 2D shapes. 

 

Program Committees: ECCV’04, CVPR’04. CVPR’05, ECCV’06, CVPR’06, EMMCVPR’07.

 

Publications

·  E. Sharon, M. Galun, D. Sharon, R. Basri, A. Brandt, Hierarchy and Adaptivity in Segmenting Visual Scenes (pdf), Nature 2006.  (June 28th online; August 17th print)

      http://www.nature.com/nature/journal/v442/n7104/index.html 

·  J. Corso, E. Sharon, A. Yuille, Multilevel Segmentation and Integrated Bayesian Model Classification with an Application to Brain Tumor Segmentation, Medical Image Computing and Computer- Assisted Intervention (MICCAI), 2006. Appeared in Springer’s  "Lecture Notes in Computer Science" (http://dx.doi.org/10.1007/11866763_97, pdf).

·  J. J. Corso, E. Sharon, S. Dube, S. El-Saden, U. Sinha and A. Yuille, Efficient Multilevel Brain Tumor Segmentation with Integrated Bayesian Model Classification (pdf), IEEE Transactions on Medical Imaging, 27(5): 629-640, 2008.

·  L. Gorelick, M. Galun, E. Sharon, R. Basri, A. Brandt, Shape Representation and Classification Using the Poisson Equation (pdf), IEEE Transactions on Pattern Analysis and Machine Intelligence, December 2006.

·  E. Sharon and D. Mumford, 2D-Shape Analysis using Conformal Mapping (pdf), International Journal of Computer Vision, Volume 70, Number 1 / October, 2006, pages 55-75;

    (special issue on CVPR’04 for papers recommended by the award committee; direct link at - http://www.springerlink.com/content/g253522836rt7644/?p=70a2fcd9035b4b02bb67c88fd51291ca&pi=4)

·  E. Borenstein, E. Sharon, S. Ullman, Combining Top-Down and Bottom-Up Segmentation (pdf), Proceedings IEEE workshop on Perceptual Organization in Computer Vision, IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, June 2004.

·  E. Sharon and D. Mumford, 2D-Shape Analysis using Conformal Mapping (pdf), Proceedings IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, June 2004.

·  L. Gorelick, M. Galun, E. Sharon, R. Basri, A. Brandt, Shape Representation and Classification Using the Poisson Equation (pdf), Proceedings IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, June 2004.

·  M. Galun, E. Sharon, R. Basri, A. Brandt, Texture Segmentation by Multiscale Aggregation of Filter Responses and Shape Elements (pdf), Proceedings IEEE International Conference on Computer Vision,  716-723, Nice ,France, 2003.

·  E. Sharon, A. Brandt, R. Basri, Segmentation and Boundary Detection Using Multiscale Intensity Measurements (ps, pdf), Proceedings IEEE Conference on Computer Vision and Pattern Recognition, I:469-476, Kauai, Hawaii, 2001. (patent pending)

·  E. Sharon, A. Brandt, R. Basri, Fast Multiscale Image Segmentation (ps, pdf) Proceedings IEEE Conference on Computer Vision and Pattern Recognition, I:70-77, South Carolina, 2000. (patent pending)

·  E. Sharon, A. Brandt, R. Basri, Completion Energies and Scale (ps, pdf), IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(10):1117-1131, October 2000.

·  E. Sharon, A. Brandt, R. Basri, Completion Energies and Scale (ps), Proceedings IEEE  Conference on Computer Vision and Pattern Recognition, 884-890, Puerto Rico, 1997.

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Weizmann Institute – ‘Interface’

Fall 2003 - Eitan Sharon, Meirav Galun, Ronen Basri and Achi Brandt: “Teaching computers to see”

 

A BBC Radio Interview

Feb 16th, 2004 – Eitan Sharon: “Teaching computers to see”

 

 VideoSurf is dedicated to creating a better way for users to search, discover and watch online videos. Using a unique combination of new computer vision and fast computation methods, VideoSurf has taught computers to “see” inside videos to find content in a fast, efficient, and scalable way. Basing its search on visual identification, rather than text only, VideoSurf’s computer vision video search engine provides more relevant results and a better experience to let users find and discover the videos they really want to watch.  Whether you’re looking to watch funny videos or scary videos, movie clips or TV full episodes, the hottest new music videos or breaking news video clips, VideoSurf’s video search engine is the place to go to find the videos you’ll love.  Spend less time searching and more time being entertained!