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Detection performance evaluation of boosted random Ferns
Villamizar Vergel, Michael Alejandro; Moreno-Noguer, Francesc; Andrade-Cetto, Juan; Sanfeliu Cortés, Alberto
Universitat Politècnica de Catalunya. Institut de Robòtica i Informàtica Industrial; Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial; Universitat Politècnica de Catalunya. VIS - Visió Artificial i Sistemes Intel.ligents
We present an experimental evaluation of Boosted Random Ferns in terms of the detection performance and the training data. We show that adding an iterative bootstrapping phase during the learning of the object classifier, it increases its detection rates given that additional positive and negative samples are collected (bootstrapped) for retraining the boosted classifier. After each bootstrapping iteration, the learning algorithm is concentrated on computing more discriminative and robust features (Random Ferns), since the bootstrapped samples extend the training data with more difficult images. The resulting classifier has been validated in two different object datasets, yielding successful detections rates in spite of challenging image conditions such as lighting changes, mild occlusions and cluttered background.
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo
Computer vision
computer vision pattern recognition
Visió per ordinador
Classificació INSPEC::Pattern recognition::Computer vision
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/submittedVersion
info:eu-repo/semantics/conferenceObject
Springer
         

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