Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection. Paul Viola and Michael Jones, "Rapid Object Detection using a Boosted Cascade of Simple Features" ht…

In the domain of face detection the system yields detection rates comparable to the best previous systems. Paul Viola and Michael Jones, "Rapid Object Detection using a Boosted Cascade of Simple Features" https://www.cs.cmu.edu/~efros/cour…

The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. Paul Viola and Michael Jon…

The third contribution is a method for combining increasingly more complex classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. Pa…

The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers. Paul Viola and Michael Jones, "Rapid Object Detection using a Boo…

The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. Paul Viola and Michael Jones, "Rapid Object Detection using a Boosted Casca…

This work is distinguished by three key contributions. Paul Viola and Michael Jones, "Rapid Object Detection using a Boosted Cascade of Simple Features" https://www.cs.cmu.edu/~efros/courses/LBMV07/Papers/viola-cvpr-01.pdf ディープラーニン…

This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. Paul Viola and Michael Jones, "Rapid Object Detection using a Boosted …