Eran Borenstein

eran_img 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 Brown University

Office: Room 310, 182 George St, Providence, RI 02912
Phone: (401) 863-7422
email: mailto

Main interests

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.

Academic Background


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!



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.

Horse Database

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