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» An Approximation for Normal Vectors of Deformable Models
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CVPR
2008
IEEE
14 years 7 months ago
Conditional density learning via regression with application to deformable shape segmentation
Many vision problems can be cast as optimizing the conditional probability density function p(C|I) where I is an image and C is a vector of model parameters describing the image. ...
Jingdan Zhang, Shaohua Kevin Zhou, Dorin Comaniciu...
NIPS
2003
13 years 6 months ago
Laplace Propagation
We present a novel method for approximate inference in Bayesian models and regularized risk functionals. It is based on the propagation of mean and variance derived from the Lapla...
Alexander J. Smola, Vishy Vishwanathan, Eleazar Es...
CORR
2008
Springer
179views Education» more  CORR 2008»
13 years 5 months ago
Distributed Parameter Estimation in Sensor Networks: Nonlinear Observation Models and Imperfect Communication
The paper studies the problem of distributed static parameter (vector) estimation in sensor networks with nonlinear observation models and imperfect inter-sensor communication. We...
Soummya Kar, José M. F. Moura, Kavita Raman...
CVPR
2012
IEEE
11 years 8 months ago
Finite Element based sequential Bayesian Non-Rigid Structure from Motion
Navier’s equations modelling linear elastic solid deformations are embedded within an Extended Kalman Filter (EKF) to compute a sequential Bayesian estimate for the Non-Rigid St...
Antonio Agudo, Begoña Calvo, J. M. M. Monti...
VTS
1997
IEEE
90views Hardware» more  VTS 1997»
13 years 9 months ago
SHOrt voltage elevation (SHOVE) test for weak CMOS ICs
A stress procedure for reliability screening, SHOrt Voltage Elevation (SHOVE) test, is analyzed here. During SHOVE, test vectors are run at higher-than-normal supply voltage for a...
Jonathan T.-Y. Chang, Edward J. McCluskey