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» Preference learning with Gaussian processes
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CVPR
2012
IEEE
11 years 9 months ago
Weakly supervised structured output learning for semantic segmentation
We address the problem of weakly supervised semantic segmentation. The training images are labeled only by the classes they contain, not by their location in the image. On test im...
Alexander Vezhnevets, Vittorio Ferrari, Joachim M....
CORR
2011
Springer
219views Education» more  CORR 2011»
13 years 2 months ago
Active Markov Information-Theoretic Path Planning for Robotic Environmental Sensing
Recent research in multi-robot exploration and mapping has focused on sampling environmental fields, which are typically modeled using the Gaussian process (GP). Existing informa...
Kian Hsiang Low, John M. Dolan, Pradeep K. Khosla
GI
2007
Springer
14 years 1 months ago
Background Modeling Using Adaptive Cluster Density Estimation for Automatic Human Detection
: Detection is an inherent part of every advanced automatic tracking system. In this work we focus on automatic detection of humans by enhanced background subtraction. Background s...
Harish Bhaskar, Lyudmila Mihaylova, Simon Maskell
ICCV
2005
IEEE
14 years 1 months ago
Priors for People Tracking from Small Training Sets
We advocate the use of Scaled Gaussian Process Latent Variable Models (SGPLVM) to learn prior models of 3D human pose for 3D people tracking. The SGPLVM simultaneously optimizes a...
Raquel Urtasun, David J. Fleet, Aaron Hertzmann, P...
BMVC
2001
13 years 9 months ago
A SOM Based Approach to Skin Detection with Application in Real Time Systems
A large body of human image processing techniques use skin detection as a first primitive for subsequent feature extraction. Well established methods of colour modelling, such as...
David A. Brown, Ian Craw, Julian Lewthwaite