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TIP
2010
141views more  TIP 2010»
13 years 4 months ago
Efficient Particle Filtering via Sparse Kernel Density Estimation
Particle filters (PFs) are Bayesian filters capable of modeling nonlinear, non-Gaussian, and nonstationary dynamical systems. Recent research in PFs has investigated ways to approp...
Amit Banerjee, Philippe Burlina
ICCV
2001
IEEE
15 years 20 hour ago
Determining Reflectance Parameters and Illumination Distribution from a Sparse Set of Images for View-dependent Image Synthesis
A framework for photo-realistic view-dependent image synthesis of a shiny object from a sparse set of images and a geometric model is proposed. Each image is aligned with the 3D m...
Ko Nishino, Zhengyou Zhang, Katsushi Ikeuchi
JMIV
2011
179views more  JMIV 2011»
13 years 5 months ago
3-D Data Denoising and Inpainting with the Low-Redundancy Fast Curvelet Transform
In this paper, we first present a new implementation of the 3-D fast curvelet transform, which is nearly 2.5 less redundant than the Curvelab (wrapping-based) implementation as o...
A. Woiselle, Jean-Luc Starck, Jalal Fadili
ICIP
2009
IEEE
13 years 7 months ago
Two-dimensional geometric lifting
Wavelets provide a sparse representation for piecewise smooth signals in 1-D; however, separable extensions of wavelets to multiple dimensions do not achieve the same level of spa...
Joshua Blackburn, Minh N. Do
JMLR
2010
202views more  JMLR 2010»
13 years 4 months ago
Learning the Structure of Deep Sparse Graphical Models
Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidd...
Ryan Prescott Adams, Hanna M. Wallach, Zoubin Ghah...