||I am currently a VP of computer vision at Videosurf, a company that applies computer vision technology to a search engine for videos, where
I am leading the development of the face-detection and recognition technology.
I was a Visiting scientist at the
Division of Applied Mathematics
Office: Room 310, 182 George St, Providence, RI 02912
Phone: (401) 863-7422
My research focuses on machine and human vision, in particular object
classification and recognition, figure-ground segmentation, the interaction
between top-down and bottom-up visual processes and other problems related to
perceptual organization. The aim is to better understand and imitate the
processes of human vision through mathematical models that account for our
ability to know "what" is in an image and "where" it is.
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, discover and watch the videos they really want to see. 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, 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!
Mathematical methods in the brain sciences (Spring 2007).
- Introduction to Vision, Open University of Israel (2003)
- Context, Computation, and Optimal ROC Performance in Hierarchical Models
L. Chang, Y. Jin, W. Zhang, E. Borenstein and S. Geman
International Journal of Computer Vision, Volume 93, Number 2
- Combined Top-Down/Bottom-Up Segmantation
E. Borenstein, S. Ullman.
IEEE Trans. Pattern Anal. Mach. Intell., 2008: 2109-2125
- Shape guided object segmentation
E. Borenstein, and J. Malik
Conference on Computer Vision and Pattern Recognition (CVPR), June 2006
- Combining Top-down and Bottom-up
E. Borenstein, E. Sharon and S. Ullman
IEEE workshop on Perceptual Organization in Computer Vision
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June
- Learning to Segment
E. Borenstein and S. Ullman
European Conference on Computer Vision (ECCV), May 2004
- Class-Specific, Top-Down
E. Borenstein and S. Ullman
European Conference on Computer Vision (ECCV), May 2002
- Lightness and Brightness Computations by
Thesis for my M.Sc degree with Shimon Ullman
Weizmann Institute of Science 2000
Segmentation Databases (with manual figure-ground labeling)
The horse, runners and cars databases were
collected via google image and then manually labelled for testing
figure-ground segmentation. For further elaboration, please refer to our
related publications above (ECCV2002, ECCV2004,CVPR2004,CVPR2006).
Please feel free to download and use these database, providing you reference
one of the above papers.
The Weizmann Horse Database consists of 328 side-view color images of
horses that were also manually segmented. The images were randomly collected
from the WWW to evaluate the top-down segmentation scheme as well as its
combination with bottom-up processing. Note however that in our experiments
we do not use RGB information and the images are given in this format just
for your conveneince.
Entire database (19.5Mb) including:
Horses (RGB) (14Mb)
Horses (grey) (4Mb)
Horses (figure-ground) (1.3Mb)
Last Updated April 24 2007