Research in our lab focus on two intimately connected branches of vision research: computer vision and human vision. In both fields, we are intrigued by visual functionalities that give rise to semantically meaningful interpretations of the visual world. In computer vision, we aspire to build intelligent visual algorithms that perform important visual perception tasks such as object recognition, scene categorization, integrative scene understanding, human motion recognition, material recognition, etc. In human vision, our curiosity leads us to study the underlying neural mechanisms that enable the human visual system to perform high level visual tasks with amazing speed and efficiency.
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| news and events [news archive] | |
| 2009.10 | Software and data of Towards Total Scene Understanding:Classification, Annotation and Segmentation in an Automatic Framework released. Click here for more details |
| 2009.09 | Congratulations to two computational neuroscience papers accepted by NIPS 2009. Click here for more details |
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Check out our newest image ontology -- ImageNet! |
| 2009.07 | BAVM 2009: Bay Area Vision Meeting on Image and Video Understanding. |
| 2009.06 | Congratulations to the Nature paper by M. Peelen, L. Fei-Fei and S. Kastner. |
| 2009.06 | Congratulations to the ICCV 2009 oral presentation by H. Su, M. Sun, L. Fei-Fei and S. Savarese. |
| press coverage | |
| 2007.08 | News articles related to Team OPTIMOL (UIUC-Princeton) at the Semantic Robot Vision Challange: 1) New Scientist magazine (full article); 2) UIUC ECE Dept. news |
| 2007.06 | "Taking the scenic route", companion article of Princeton EQUAD News "Frontiers of Health", School of Engineering and Applied Sciences, Princeton. |
| 2006.05.03 | "Recognizing the brightest minds in computer science," Microsoft press release |
| 2006.04.26 | "Microsoft Research recognizes computer science's most promising professors with New Faculty Fellowships," Microsoft press release |