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CVPR
2007
IEEE
14 years 9 months ago
Learning the Compositional Nature of Visual Objects
The compositional nature of visual objects significantly limits their representation complexity and renders learning of structured object models tractable. Adopting this modeling ...
Björn Ommer, Joachim M. Buhmann
ICRA
2010
IEEE
101views Robotics» more  ICRA 2010»
13 years 6 months ago
Searching for objects: Combining multiple cues to object locations using a maximum entropy model
— In this paper, we consider the problem of how background knowledge about usual object arrangements can be utilized by a mobile robot to more efficiently find an object in an ...
Dominik Joho, Wolfram Burgard
CVPR
2009
IEEE
1413views Computer Vision» more  CVPR 2009»
15 years 2 months ago
Learning Semantic Scene Models by Object Classification and Trajectory Clustering
The visual surveillance task is to monitor the activity of objects in a scene. In far-field settings (i.e., wide outdoor areas), the majority of visible activities are objects movi...
Hanqing Lu, Stan Z. Li, Tianzhu Zhang
ICVS
2009
Springer
14 years 2 months ago
Learning Objects and Grasp Affordances through Autonomous Exploration
Abstract. We describe a system for autonomous learning of visual object representations and their grasp affordances on a robot-vision system. It segments objects by grasping and mo...
Dirk Kraft, Renaud Detry, Nicolas Pugeault, Emre B...
TNN
2008
178views more  TNN 2008»
13 years 7 months ago
IMORL: Incremental Multiple-Object Recognition and Localization
This paper proposes an incremental multiple-object recognition and localization (IMORL) method. The objective of IMORL is to adaptively learn multiple interesting objects in an ima...
Haibo He, Sheng Chen