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Computational statistics / AdaBoost / Haar-like features / Statistical classification / Boosting / Face detection / Classifier / Boosting methods for object categorization / Viola–Jones object detection framework / Artificial intelligence / Ensemble learning / Learning


Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection Rainer Lienhart, Alexander Kuranov, Vadim Pisarevsky Microprocessor Research Lab, Intel Labs Intel Corporation, Santa Clara, CA 9
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Document Date: 2011-06-17 18:04:59

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File Size: 1,03 MB

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City

San Francisco / Copenhagen / Santa Clara / /

Company

Neural Networks / Intel Labs Intel Corporation / /

Country

Denmark / /

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Facility

Open Computer Vision Library / /

IndustryTerm

object detection algorithms / face detection systems / plain vanilla solution / multi-scale image search / machine learning algorithm / learning algorithm / respective boosting algorithm / /

MarketIndex

set 45 / Set 130 / /

Organization

Vadim Pisarevsky Microprocessor Research Lab / Pattern Analysis and Machine Intelligence / /

Person

Morgan Kauman / Jochen Maydt / Harry Shum / Rainer Lienhart / Michael J. Jones / Andrew Blake / Stan Z. Li / Paul Viola / Vadim Pisarevsky Microprocessor / /

Position

representative / /

Product

Pentax K-x Digital Camera / /

ProvinceOrState

California / /

PublishedMedium

IEEE Transactions on Pattern Analysis and Machine Intelligence / Machine Learning / /

Technology

learning algorithm / Machine Learning / two boosting algorithm / Neural network / object detection algorithms / machine learning algorithm / respective boosting algorithm / /

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