Show Reference: "Rapid object detection using a boosted cascade of simple features"

Rapid object detection using a boosted cascade of simple features IEEE Computer Society Conference on Computer Vision and Pattern Recognition In Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, Vol. 1 (15 April 2001), pp. 511-518, doi:10.1109/cvpr.2001.990517 by Paul Viola, Michael Jones
@inproceedings{viola-and-jones-2001,
    abstract = {This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. 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. 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. 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. 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. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.},
    address = {Los Alamitos, CA, USA},
    author = {Viola, Paul and Jones, Michael},
    booktitle = {Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on},
    citeulike-article-id = {962012},
    citeulike-linkout-0 = {http://doi.ieeecomputersociety.org/10.1109/CVPR.2001.990517},
    citeulike-linkout-1 = {http://dx.doi.org/10.1109/cvpr.2001.990517},
    citeulike-linkout-2 = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=990517},
    day = {15},
    doi = {10.1109/cvpr.2001.990517},
    institution = {Mitsubishi Electr. Res. Labs., Cambridge, MA, USA},
    isbn = {0-7695-1272-0},
    issn = {1063-6919},
    journal = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
    keywords = {face-detection, visual, visual-processing},
    location = {Kauai, HI, USA},
    month = apr,
    pages = {511--518},
    posted-at = {2015-02-04 10:50:00},
    priority = {2},
    publisher = {IEEE},
    title = {Rapid object detection using a boosted cascade of simple features},
    url = {http://dx.doi.org/10.1109/cvpr.2001.990517},
    volume = {1},
    year = {2001}
}

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Viola and Jones presented a fast and robust object detection system based on

  1. a computationally fast way to extract features from images,
  2. the AdaBoost machine learning algorithm,
  3. cascades of weak classifiers with increasing complexities.