A unified learning framework for object detection and classification using nested cascades of boosted classifiers

Authors:
Rodrigo Verschae;Javier Ruiz-del-Solar;Mauricio Correa
Affiliations:
Universidad de Chile, Department of Electrical Engineering, Av. Tupper 2007, 837-0451, Santiago, Chile;Universidad de Chile, Department of Electrical Engineering, Av. Tupper 2007, 837-0451, Santiago, Chile;Universidad de Chile, Department of Electrical Engineering, Av. Tupper 2007, 837-0451, Santiago, Chile
Venue:
Machine Vision and Applications
Year:
2008

Citing 0
Cited 9

Detection of AIBO and Humanoid Robots Using Cascades of Boosted Classifiers

RoboCup 2007: Robot Soccer World Cup XI
Multiclass Adaboost and Coupled Classifiers for Object Detection

CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
A Framework for Context-Aware Adaptation in Public Displays

OTM '09 Proceedings of the Confederated International Workshops and Posters on On the Move to Meaningful Internet Systems: ADI, CAMS, EI2N, ISDE, IWSSA, MONET, OnToContent, ODIS, ORM, OTM Academy, SWWS, SEMELS, Beyond SAWSDL, and COMBEK 2009
Face and iris localization using templates designed by particle swarm optimization

Pattern Recognition Letters
Real-time hand gesture detection and recognition using boosted classifiers and active learning

PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
Accelerated classifier training using the PSL cascading structure

ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Human Detection and Identification by Robots Using Thermal and Visual Information in Domestic Environments

Journal of Intelligent and Robotic Systems
Real-time hand gesture recognition for human robot interaction

RoboCup 2009
Analyzing the human-robot interaction abilities of a general-purpose social robot in different naturalistic environments

RoboCup 2009

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper a unified learning framework for object detection and classification using nested cascades of boosted classifiers is proposed. The most interesting aspect of this framework is the integration of powerful learning capabilities together with effective training procedures, which allows building detection and classification systems with high accuracy, robustness, processing speed, and training speed. The proposed framework allows us to build state of the art face detection, eyes detection, and gender classification systems. The performance of these systems is validated and analyzed using standard face databases (BioID, FERET and CMU-MIT), and a new face database (UCHFACE).