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» Learning Hierarchical Models of Scenes, Objects, and Parts
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NN
2008
Springer
201views Neural Networks» more  NN 2008»
14 years 9 months ago
Learning representations for object classification using multi-stage optimal component analysis
Learning data representations is a fundamental challenge in modeling neural processes and plays an important role in applications such as object recognition. In multi-stage Optima...
Yiming Wu, Xiuwen Liu, Washington Mio
ECCV
2002
Springer
15 years 11 months ago
Audio-Video Sensor Fusion with Probabilistic Graphical Models
Abstract. We present a new approach to modeling and processing multimedia data. This approach is based on graphical models that combine audio and video variables. We demonstrate it...
Matthew J. Beal, Hagai Attias, Nebojsa Jojic
ICAT
2006
IEEE
15 years 3 months ago
An Animation System for Imitation of Object Grasping in Virtual Reality
Interactive virtual characters are nowadays commonplace in games, animations, and Virtual Reality (VR) applications. However, relatively few work has so far considered the animatio...
Matthias Weber, Guido Heumer, Heni Ben Amor, Bernh...
ICCV
2001
IEEE
15 years 11 months ago
Learning Image Statistics for Bayesian Tracking
This paper describes a framework for learning probabilistic models of objects and scenes and for exploiting these models for tracking complex, deformable, or articulated objects i...
Hedvig Sidenbladh, Michael J. Black
DICTA
2007
14 years 11 months ago
The Tower of Knowledge Scheme for Learning in Computer Vision
A scheme, named tower of knowledge (ToK), is proposed for interpreting 3D scenes. The ToK encapsulates causal dependencies between object appearance and functionality. We demonstr...
Maria Petrou, Mai Xu