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JCST
2010
139views more  JCST 2010»
13 years 6 months ago
Dirichlet Process Gaussian Mixture Models: Choice of the Base Distribution
In the Bayesian mixture modeling framework it is possible to infer the necessary number of components to model the data and therefore it is unnecessary to explicitly restrict the n...
Dilan Görür, Carl Edward Rasmussen
ICCV
2009
IEEE
15 years 20 days ago
Directional Statistics BRDF Model
We introduce a novel parametric BRDF model that can accurately encode a wide variety of real-world isotropic BRDFs with a small number of parameters. The key observation we make...
Ko Nishino
CVPR
2001
IEEE
14 years 9 months ago
Image-based Modeling and Rendering of Surfaces with Arbitrary BRDFs
A goal of image-based rendering is to synthesize as realistically as possible man made and natural objects. This paper presents a method for image-based modeling and rendering of ...
Melissa L. Koudelka, Peter N. Belhumeur, Sebastian...
ICIP
2006
IEEE
14 years 9 months ago
Aggregated Dynamic Background Modeling
Standard practices in background modeling learn a separate model for every pixel in the image. However, in dynamic scenes the connection between an observation and the place where...
Amit Adam, Ehud Rivlin, Ilan Shimshoni
UAI
2008
13 years 9 months ago
Church: a language for generative models
Formal languages for probabilistic modeling enable re-use, modularity, and descriptive clarity, and can foster generic inference techniques. We introduce Church, a universal langu...
Noah Goodman, Vikash K. Mansinghka, Daniel M. Roy,...