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» Learning Mixtures of DAG Models
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JFR
2006
88views more  JFR 2006»
13 years 7 months ago
Discovering natural kinds of robot sensory experiences in unstructured environments
We derive categories directly from robot sensor data to address the symbol grounding problem. Unlike model-based approaches where human intuitive correspondences are sought betwee...
Daniel H. Grollman, Odest Chadwicke Jenkins, Frank...
ECCV
2002
Springer
14 years 9 months ago
Composite Texture Descriptions
Textures can often more easily be described as a composition of subtextures than as a single texture. The paper proposes a way to model and synthesize such "composite textures...
Alexey Zalesny, Vittorio Ferrari, Geert Caenen, Do...
ICCV
2007
IEEE
14 years 9 months ago
Steerable Random Fields
In contrast to traditional Markov random field (MRF) models, we develop a Steerable Random Field (SRF) in which the field potentials are defined in terms of filter responses that ...
Stefan Roth, Michael J. Black
ICCV
2001
IEEE
14 years 9 months ago
Separating Appearance from Deformation
By representing images and image prototypes by linear subspaces spanned by "tangent vectors" (derivatives of an image with respect to translation, rotation, etc.), impre...
Nebojsa Jojic, Patrice Simard, Brendan J. Frey, Da...
ECCV
2008
Springer
14 years 9 months ago
Multi-camera Tracking and Atypical Motion Detection with Behavioral Maps
Abstract. We introduce a novel behavioral model to describe pedestrians motions, which is able to capture sophisticated motion patterns resulting from the mixture of different cate...
Jérôme Berclaz, François Fleur...