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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 ... (Update)
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BibTeX entry: (Update)
Paul Viola and Michael Jones. Rapid object detection using a boosted cascade of simple features. In CVPR, 2001. http://citeseer.ist.psu.edu/viola01rapid.html More
@misc{ viola01rapid,
author = "P. Viola and M. Jones",
title = "Rapid object detection using a boosted cascade of simple features",
text = "Paul Viola and Michael Jones. Rapid object detection using a boosted cascade
of simple features. In CVPR, 2001.",
year = "2001",
url = "citeseer.ist.psu.edu/viola01rapid.html" }
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243 Boosting the margin: A new explanation for the effectiveness.. - Schapire, Freund et al. - 1997
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84 object detection applied to faces and cars (context) - Schneiderman, Kanade et al. - 2000
74 A general framework for object detection (context) - Papageorgiou, Oren et al. - 1998
74 Summed-area tables for texture mapping (context) - Crow - 1984
52 Modeling visual-attention via selective tuning (context) - Tsotsos, Culhane et al. - 1995
49 Statistical Pattern Recognition (context) - Webb - 1999
48 Boosting image retrieval - Tieu, Viola - 2000
38 Joint induction of shape features and tree classifiers - Amit, Geman et al. - 1997
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29 Overcomplete steerable pyramid fil- ters and rotation invari.. - Greenspan, Belongie et al. - 1994
22 IEEE Transactions on Pattern Analysis and Machine Intelligen.. (context) - Freeman, Adelson et al. - 1991
15 Example-based learning for viewbased face detection (context) - Sung, Poggio - 1998
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