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Artificial intelligence / Object recognition / Shape context / Feature / Statistical classification / Scale-invariant feature transform / Boosting methods for object categorization / Computer vision / Vision / Imaging


Sign Classification using Local and Meta-Features Marwan A. Mattar, Allen R. Hanson, and Erik G. Learned-Miller Computer Vision Laboratory Department of Computer Science University of Massachusetts Amherst, MA[removed]USA
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Document Date: 2008-01-02 15:36:22

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File Size: 630,94 KB

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City

Washington / DC / Washington / D.C. / Breckenridge / Cambridge / San Diego / /

Company

National Academy Press / Vehicle-Highway Automation / MIT Press / Research Working Group / Nikon / Industrial Electronics / Advanced Traffic Management Systems / /

Country

Brazil / /

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Facility

Computer Science University of Massachusetts Amherst / Austrian Research Institute / /

IndustryTerm

classification algorithm / object recognition applications / project web page / connected components algorithm / ensemble algorithm / automatic document processing / search structure / nearest neighbor search / /

Organization

Computer Science University of Massachusetts Amherst / National Research Council / Austrian Research Institute / National Science Foundation / MIT / Committee on Vision / Univ. of Massachusetts Amherst / Erik G. Learned-Miller Computer Vision Laboratory Department / /

Person

Marwan A. Mattar / Allen R. Hanson / Dimitri Lisin / Richard Weiss / Piyanuch Silapachote / Meta-Features Marwan / Erik G. Learned-Miller / /

Position

Common-frame model for object recognition / General / /

ProvinceOrState

Massachusetts / Colorado / /

PublishedMedium

Machine Learning / /

Technology

Machine Vision / connected components algorithm / Machine Learning / Simulation / classification algorithm / SIFT algorithm / ensemble algorithm / digital camera / /

URL

http /

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