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
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Robust multipose face detection in images
IEEE Transactions on Circuits and Systems for Video Technology
Debellor: A Data Mining Platform with Stream Architecture
Transactions on Rough Sets IX
A novel multiplex cascade classifier for pedestrian detection
Pattern Recognition Letters
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Object detection using AdaBoost cascade classifier was introduced by Viola and Jones in December 2001. This paper presents a modification of their method which allows to obtain even 4-fold decrease in false rejection rate, keeping false acceptance rate - as well as the classifier size and training time - at the same level. Such an improvement is achieved by extending original family of weak classifiers, which is searched through in every step of AdaBoost algorithm, with classifiers calculating absolute value of contrast. Test results given in the paper come from a face localization problem, but the idea of absolute contrasts can be applied to detection of other types of objects, as well.