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» Learning Models for Object Recognition
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SCIA
2005
Springer
211views Image Analysis» more  SCIA 2005»
14 years 2 months ago
Perception-Action Based Object Detection from Local Descriptor Combination and Reinforcement Learning
This work proposes to learn visual encodings of attention patterns that enables sequential attention for object detection in real world environments. The system embeds a saccadic d...
Lucas Paletta, Gerald Fritz, Christin Seifert
JMLR
2010
169views more  JMLR 2010»
13 years 3 months ago
Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images
Deep belief nets have been successful in modeling handwritten characters, but it has proved more difficult to apply them to real images. The problem lies in the restricted Boltzma...
Marc'Aurelio Ranzato, Alex Krizhevsky, Geoffrey E....
ICML
2005
IEEE
14 years 10 months ago
Active learning for Hidden Markov Models: objective functions and algorithms
Hidden Markov Models (HMMs) model sequential data in many fields such as text/speech processing and biosignal analysis. Active learning algorithms learn faster and/or better by cl...
Brigham Anderson, Andrew Moore
ICRA
2008
IEEE
204views Robotics» more  ICRA 2008»
14 years 3 months ago
Active exploration and keypoint clustering for object recognition
— Object recognition is a challenging problem for artificial systems. This is especially true for objects that are placed in cluttered and uncontrolled environments. To challenge...
Gert Kootstra, Jelmer Ypma, Bart de Boer
HLK
2003
IEEE
14 years 2 months ago
Regularized 3D Morphable Models
Three-dimensional morphable models of object classes are a powerful tool in modeling, animation and recognition. We introduce here the new concept of regularized 3D morphable mode...
Curzio Basso, Thomas Vetter, Volker Blanz