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DICTA
2009
13 years 6 months ago
SIFTing the Relevant from the Irrelevant: Automatically Detecting Objects in Training Images
Many state-of-the-art object recognition systems rely on identifying the location of objects in images, in order to better learn its visual attributes. In this paper, we propose fo...
Edmond Zhang, Michael Mayo
ECCV
2004
Springer
14 years 7 months ago
Weak Hypotheses and Boosting for Generic Object Detection and Recognition
In this paper we describe the first stage of a new learning system for object detection and recognition. For our system we propose Boosting [5] as the underlying learning technique...
Andreas Opelt, Michael Fussenegger, Axel Pinz, Pet...
CVPR
2008
IEEE
14 years 7 months ago
Viewpoint-independent object class detection using 3D Feature Maps
This paper presents a 3D approach to multi-view object class detection. Most existing approaches recognize object classes for a particular viewpoint or combine classifiers for a f...
Joerg Liebelt, Cordelia Schmid, Klaus Schertler
GECCO
2010
Springer
232views Optimization» more  GECCO 2010»
13 years 6 months ago
Genetic algorithms for automatic classification of moving objects
This paper presents an integrated approach, combining a state-of-the-art commercial object detection system and genetic algorithms (GA)-based learning for automatic object classif...
Omid David-Tabibi, Nathan S. Netanyahu, Yoav Rosen...
CVPR
2005
IEEE
14 years 7 months ago
A Statistical Field Model for Pedestrian Detection
This paper presents a new statistical model for detecting and tracking deformable objects such as pedestrians, where large shape variations induced by local shape deformation can ...
Ying Wu, Ting Yu, Gang Hua