Multiclass Adaboost and Coupled Classifiers for Object Detection
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
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International Journal of Computer Vision
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AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Topology modeling for Adaboost-cascade based object detection
Pattern Recognition Letters
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ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
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The Journal of Machine Learning Research
Computer Vision and Image Understanding
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ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
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Signal Processing
Exploiting features: locally interleaved sequential alignment for object detection
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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Machine Vision and Applications
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We describe a method for training object detectors using a generalization of the cascade architecture, which results in a detection rate and speed comparable to that of the best published detectors while allowing for easier training and a detector with fewer features. In addition, the method allows for quickly calibrating the detector for a target detection rate, false positive rate or speed. One important advantage of our method is that it enables systematic exploration of the ROC Surface, which characterizes the trade-off between accuracy and speed for a given classifier.