Overview:
We are interested in algorithms that can find objects in images and
video. Such algorithms have practical implications for applications
such as image search and robotic navigation. Important and open
issues include representation and learning - what makes a good model
of an object, and how can one learn such a representation? To help
answer such questions, we employ both generative and discriminative
machine-learning techniques.
Publications:
Ramanan, D.
Cvpr, 2007, p. 1-8
Ramanan, Deva; Baker, Simon; Kakade, Sham
Iccv, 2007, 141,
Ramanan, Deva
Advances in neural information processing systems 19, 2007, p. 1129--1136
Ren, Xiaofeng; Fowlkes, Charless; Malik, Jitendra
Advances in neural information processing systems 18, 2006, p. 1121--1128
Gehler, P. V.; Holub, A. D.; Welling, M.
Proceedings of the 23rd International Conference on Machine Learning (ICML 2006), 2006, p. 337-344
Ramanan, D.; Sminchisescu, C.
Cvpr, 2006, p. I: 206-213
Ramanan, D.; Forsyth, D.A.; Barnard, K.
PAMI, 2006, 28, 288, p. 1319-1334
Ramanan, D.; Forsyth, D.A.; Zisserman, A.
Cvpr, 2005, p. I: 271-278
Holub, A.D.; Welling, M.; Perona, P.
Iccv, 2005, p. I: 136-143
Ramanan, D.; Forsyth, D.A.; Barnard, K.
Cvpr, 2005, p. II: 635-642
Ramanan, D.; Forsyth, D.A.
Iccv, 2003, p. 338-345
Weber, M.; Einhaeuser, W.; Welling, M.; Perona, P.
Afgr, 2000, p. 20-27
Weber, M.; Welling, M.; Perona, P.
Cvpr, 2000, p. II: 101-108
Weber, M.; Welling, M.; Perona, P.
Eccv, 2000, p. I: 18-32
Weber, M.; Welling, M.; Perona, P.
Jnsc, 1999,
Computational Vision at UC Irvine
