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» The Dark Side of Object Learning: Learning Objects
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CVPR
2010
IEEE
13 years 7 months ago
P-N learning: Bootstrapping binary classifiers by structural constraints
This paper shows that the performance of a binary classifier can be significantly improved by the processing of structured unlabeled data, i.e. data are structured if knowing the ...
Zdenek Kalal, Jiri Matas, Krystian Mikolajczyk
IROS
2006
IEEE
247views Robotics» more  IROS 2006»
14 years 3 months ago
Towards Open-Ended 3D Rotation and Shift Invariant Object Detection for Robot Companions
- Robot companions need to be able to constantly acquire knowledge about new objects for instance in order to detect them in the environment. This ability is necessary since it is ...
Jens Kubacki, Winfried Baum
ICPR
2006
IEEE
14 years 10 months ago
Object and Scene Classification: what does a Supervised Approach Provide us?
Given a set of images of scenes containing different object categories (e.g. grass, roads) our objective is to discover these objects in each image, and to use this object occurre...
Anna Bosch, Arnau Oliver, Robert Marti, Xavier Mu&...
CVPR
2000
IEEE
14 years 11 months ago
Towards Automatic Discovery of Object Categories
We propose a method to learn heterogeneous models of object classes for visual recognition. The training images contain a preponderance of clutter and learning is unsupervised. Ou...
Markus Weber, Max Welling, Pietro Perona
ICMCS
2005
IEEE
182views Multimedia» more  ICMCS 2005»
14 years 2 months ago
An integrated approach for generic object detection using kernel PCA and boosting
In this paper we present a novel framework for generic object class detection by integrating Kernel PCA with AdaBoost. The classifier obtained in this way is invariant to changes...
Saad Ali, Mubarak Shah