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» Learning Models for Object Recognition
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ICML
2005
IEEE
14 years 9 months ago
Q-learning of sequential attention for visual object recognition from informative local descriptors
This work provides a framework for learning sequential attention in real-world visual object recognition, using an architecture of three processing stages. The first stage rejects...
Lucas Paletta, Gerald Fritz, Christin Seifert
IVC
2008
182views more  IVC 2008»
13 years 9 months ago
Ontology based complex object recognition
This paper presents an object categorization method. Our approach involves the following aspects of cognitive vision : machine learning and knowledge representation. A major eleme...
Nicolas Maillot, Monique Thonnat
DSMML
2004
Springer
14 years 2 months ago
Object Recognition via Local Patch Labelling
Abstract. In recent years the problem of object recognition has received considerable attention from both the machine learning and computer vision communities. The key challenge of...
Christopher M. Bishop, Ilkay Ulusoy
ICDAR
2003
IEEE
14 years 2 months ago
Unsupervised Feature Selection Using Multi-Objective Genetic Algorithms for Handwritten Word Recognition
In this paper a methodology for feature selection in unsupervised learning is proposed. It makes use of a multiobjective genetic algorithm where the minimization of the number of ...
Marisa E. Morita, Robert Sabourin, Flávio B...
ECTEL
2007
Springer
14 years 3 months ago
Model Driven E-Learning Platform Integration
The success of the e-learning paradigm observed in recent times created a growing demand for e-learning systems in universities and other educational institutions, that itself led ...
Zuzana Bizonova