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» Learning Low-Level Vision
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CVPR
2011
IEEE
13 years 5 months ago
Modeling the joint density of two images under a variety of transformations
We describe a generative model of the relationship between two images. The model is defined as a factored threeway Boltzmann machine, in which hidden variables collaborate to de...
Joshua Susskind, Roland Memisevic, Geoffrey Hinton...
CVPR
2011
IEEE
13 years 1 months ago
Nonlinear Shape Manifolds as Shape Priors in Level Set Segmentation and Tracking
We propose a novel nonlinear, probabilistic and variational method for adding shape information to level setbased segmentation and tracking. Unlike previous work, we represent sha...
Victor Prisacariu, Ian Reid
CVPR
2012
IEEE
12 years 3 days ago
Robust Boltzmann Machines for recognition and denoising
While Boltzmann Machines have been successful at unsupervised learning and density modeling of images and speech data, they can be very sensitive to noise in the data. In this pap...
Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hi...
ICCV
2003
IEEE
14 years 11 months ago
A Sparse Probabilistic Learning Algorithm for Real-Time Tracking
This paper addresses the problem of applying powerful pattern recognition algorithms based on kernels to efficient visual tracking. Recently Avidan [1] has shown that object recog...
Oliver M. C. Williams, Andrew Blake, Roberto Cipol...
PAMI
2007
245views more  PAMI 2007»
13 years 9 months ago
Tracking People by Learning Their Appearance
—An open vision problem is to automatically track the articulations of people from a video sequence. This problem is difficult because one needs to determine both the number of p...
Deva Ramanan, David A. Forsyth, Andrew Zisserman