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» Object Classification in Visual Surveillance Using Adaboost
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AAAI
1996
15 years 1 months ago
A Hybrid Learning Approach for Better Recognition of Visual Objects
Real world images often contain similar objects but with different rotations, noise, or other visual alterations. Vision systems should be able to recognize objects regardless of ...
Ibrahim F. Imam, Srinivas Gutta
CVPR
2009
IEEE
16 years 6 months ago
Reducing JointBoost-Based Multiclass Classification to Proximity Search
Boosted one-versus-all (OVA) classifiers are commonly used in multiclass problems, such as generic object recognition, biometrics-based identification, or gesture recognition. Join...
Alexandra Stefan (University of Texas at Arlington...
CIVR
2008
Springer
227views Image Analysis» more  CIVR 2008»
15 years 1 months ago
A comparison of color features for visual concept classification
Concept classification is important to access visual information on the level of objects and scene types. So far, intensity-based features have been widely used. To increase discr...
Koen E. A. van de Sande, Theo Gevers, Cees G. M. S...
97
Voted
ISORC
2006
IEEE
15 years 5 months ago
Individual Contour Extraction for Robust Wide Area Target Tracking in Visual Sensor Networks
In this paper, we propose an approach to collaboratively track motion of a moving target in a wide area utilizing camera-equipped visual sensor networks, which are expected to pla...
Xiaoling Wu, Hoon Heo, Riaz Ahmed Shaikh, Jinsung ...
86
Voted
ICPR
2008
IEEE
16 years 26 days ago
A method of feature selection using contribution ratio based on boosting
AdaBoost and support vector machines (SVM) algorithms are commonly used in the field of object recognition. As classifiers, their classification performance is sensitive to affect...
Masamitsu Tsuchiya, Hironobu Fujiyoshi