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» A General Method for Feature Matching and Model Extraction
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CVPR
2011
IEEE
15 years 19 days ago
On Deep Generative Models with Applications to Recognition
The most popular way to use probabilistic models in vision is first to extract some descriptors of small image patches or object parts using well-engineered features, and then to...
Marc', Aurelio Ranzato, Joshua Susskind, Volodymyr...
MIR
2003
ACM
147views Multimedia» more  MIR 2003»
15 years 9 months ago
Model checking for detection of sport highlights
Automatic semantic annotation of sports video requires that the domain knowledge is properly included and exploited in the annotation process and that low and intermediate-level f...
Marco Bertini, Alberto Del Bimbo, Walter Nunziati
KDD
2010
ACM
274views Data Mining» more  KDD 2010»
15 years 8 months ago
Grafting-light: fast, incremental feature selection and structure learning of Markov random fields
Feature selection is an important task in order to achieve better generalizability in high dimensional learning, and structure learning of Markov random fields (MRFs) can automat...
Jun Zhu, Ni Lao, Eric P. Xing
AIPR
2002
IEEE
15 years 9 months ago
ICA Mixture Model based Unsupervised Classification of Hyperspectral Imagery
Conventional remote sensing classification techniques that model the data in each class with a multivariate Gaussian distribution are inefficient, as this assumption is generally ...
Chintan A. Shah, Manoj K. Arora, Stefan A. Robila,...
VMV
2003
203views Visualization» more  VMV 2003»
15 years 5 months ago
Semantic Scene Analysis of Scanned 3D Indoor Environments
Precise digital 3D models of indoor environments are needed in several applications, e.g., facility management, architecture, rescue and inspection robotics. This paper presents a...
Andreas Nüchter, Hartmut Surmann, Kai Lingema...