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» Online Selection of Tracking Features using AdaBoost
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88
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ICCV
2007
IEEE
15 years 11 months ago
Gradient Feature Selection for Online Boosting
Boosting has been widely applied in computer vision, especially after Viola and Jones's seminal work [23]. The marriage of rectangular features and integral-imageenabled fast...
Ting Yu, Xiaoming Liu 0002
ICIP
2009
IEEE
15 years 10 months ago
Online Subjective Feature Selection For Occlusion Management In Tracking Applications
Most of the state-of-the-art tracking algorithms are prone to error when dealing with occlusions, especially when the involved moving objects are hardly discernible in appearance....
CVPR
2007
IEEE
15 years 11 months ago
Learning Features for Tracking
We treat tracking as a matching problem of detected keypoints between successive frames. The novelty of this paper is to learn classifier-based keypoint descriptions allowing to i...
Michael Grabner, Helmut Grabner, Horst Bischof
201
Voted
MVA
2011
336views Computer Vision» more  MVA 2011»
14 years 4 months ago
In-vehicle camera traffic sign detection and recognition
: In this paper we discuss theoretical foundations and a practical realization of a real-time traffic sign detection, tracking and recognition system operating on board of a vehicl...
Andrzej Ruta, Fatih Porikli, Shintaro Watanabe, Yo...
KDD
2006
ACM
170views Data Mining» more  KDD 2006»
15 years 10 months ago
Computer aided detection via asymmetric cascade of sparse hyperplane classifiers
This paper describes a novel classification method for computer aided detection (CAD) that identifies structures of interest from medical images. CAD problems are challenging larg...
Jinbo Bi, Senthil Periaswamy, Kazunori Okada, Tosh...