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» Robust Boosting for Learning from Few Examples
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CVPR
2004
IEEE
14 years 9 months ago
Learning Object Detection from a Small Number of Examples: The Importance of Good Features
Face detection systems have recently achieved high detection rates[11, 8, 5] and real-time performance[11]. However, these methods usually rely on a huge training database (around...
Kobi Levi, Yair Weiss
ICPR
2006
IEEE
14 years 8 months ago
Boosted Gabor Features Applied to Vehicle Detection
Robust vehicle detection is a challenging task given vehicles with different types, and sizes, and at different distances. This paper proposes a Boosted Gabor Features (BGF) appro...
Chong Sun, Hong Cheng, Nanning Zheng
ECCV
2010
Springer
14 years 23 days ago
Robust Multi-View Boosting with Priors
Many learning tasks for computer vision problems can be described by multiple views or multiple features. These views can be exploited in order to learn from unlabeled data, a.k.a....
ICRA
2005
IEEE
122views Robotics» more  ICRA 2005»
14 years 1 months ago
Supervised Learning of Places from Range Data using AdaBoost
— This paper addresses the problem of classifying places in the environment of a mobile robot into semantic categories. We believe that semantic information about the type of pla...
Óscar Martínez Mozos, Cyrill Stachni...
NIPS
2001
13 years 8 months ago
Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade
This paper develops a new approach for extremely fast detection in domains where the distribution of positive and negative examples is highly skewed (e.g. face detection or databa...
Paul A. Viola, Michael J. Jones