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» Learning the Structure of Deep Sparse Graphical Models
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ICCV
2005
IEEE
14 years 2 months ago
Learning Hierarchical Models of Scenes, Objects, and Parts
We describe a hierarchical probabilistic model for the detection and recognition of objects in cluttered, natural scenes. The model is based on a set of parts which describe the e...
Erik B. Sudderth, Antonio B. Torralba, William T. ...
DCC
2007
IEEE
14 years 8 months ago
Spatial Sparsity Induced Temporal Prediction for Hybrid Video Compression
In this paper we propose a new motion compensated prediction technique that enables successful predictive encoding during fades, blended scenes, temporally decorrelated noise, and...
Gang Hua, Onur G. Guleryuz
JETAI
1998
110views more  JETAI 1998»
13 years 8 months ago
Independency relationships and learning algorithms for singly connected networks
Graphical structures such as Bayesian networks or Markov networks are very useful tools for representing irrelevance or independency relationships, and they may be used to e cientl...
Luis M. de Campos
VISUALIZATION
2005
IEEE
14 years 2 months ago
Build-by-Number: Rearranging the Real World to Visualize Novel Architectural Spaces
We present Build-by-Number, a technique for quickly designing architectural structures that can be rendered photorealistically at interactive rates. We combine image-based capturi...
Daniel R. Bekins, Daniel G. Aliaga
ICAT
2003
IEEE
14 years 1 months ago
Distance Learning of Chang'an in an Immersive Environment
We have made a computer graphics model of Chang'an City when it was the capital of China during the Tang Dynasty (7-10th century). The real-time rendered images are projected...
Miho Kobayashi, Kei Utsugi, Masami Yamasaki, Haruo...