A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Saliency, Scale and Image Description
International Journal of Computer Vision
Boosting Chain Learning for Object Detection
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Inter-Stage Feature Propagation in Cascade Building with AdaBoost
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Real-Time Face Detection Using Boosting in Hierarchical Feature Spaces
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
WaldBoost " Learning for Time Constrained Sequential Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Joint Haar-like Features for Face Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Learning a fast emulator of a binary decision process
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Adaboost with totally corrective updates for fast face detection
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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Detection of patterns in images with classifiers is currently one of the most important research topics in computer vision. Many practical applications such as face detection exist and recent work even suggests that any specialized detectors (e.g. corner-point detectors) can be approximated by very fast detection classifiers. In this paper, we analyze the requirements on tools which are needed when experimenting with detection classifiers and we present a general framework which was created to fulfill these requirements. This framework offers high performance for training, high variability, elegant handling of configuration and it is able to meet all the requirements which arise when experimenting with almost all possible kinds of detection classifiers. The framework offers good testing support, full supporting infrastructure and some useful training algorithms and features. We offer this framework for research and educational purposes and we hope it will allow lower initial investments when experimenting with detection classifiers.