Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
A Brief Introduction to Boosting
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Robust Real-Time Face Detection
International Journal of Computer Vision
FloatBoost Learning and Statistical Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Object Detection via Soft Cascade
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
High-Performance Rotation Invariant Multiview Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Design of Cascades of Boosted Ensembles for Face Detection
International Journal of Computer Vision
Evidence Contrary to the Statistical View of Boosting
The Journal of Machine Learning Research
Toward Accurate and Fast Iris Segmentation for Iris Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
UBIRIS: a noisy iris image database
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Hi-index | 0.10 |
Several important issues involved in Adaboost-cascade learning still remain open problems. In this work, several novel ideas are proposed for improved Adaboost-cascade object detection. The most important one is the novel topology oriented Adaboost (TOBoost) algorithm. TOBoost immediately minimizes the classification error of each selected feature, and thus enables the final detector to be more discriminative and to converge more quickly. Moreover, a simple cascading scheme is presented for tuning the cascade parameters of TOBoost; and Gaussian kernel density estimation is introduced to enhance the generalization ability of TOBoost. Another important contribution is the topology modeling of Haar-like (HL) features, which reveals an interesting property of negative HL features and significantly avoids unnecessary training computations. Non-adjacent Haar-like features are consequently configured for more effective object representation. The above enhancements result in a more efficient and stable detector with fewer features. Extensive experiments in the application of iris detection are conducted and encouraging performance is achieved.