Detection performance evaluation of boosted random Ferns
Cita com:
hdl:2117/15427
Document typeConference report
Defense date2011
PublisherSpringer
Rights accessOpen Access
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Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
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.
CitationVillamizar, M.A. [et al.]. Detection performance evaluation of boosted random Ferns. A: Iberian conference on pattern recognition and image analysis. "Lecture Notes in Computer Science, 2011, Volume 6669/2011". Las Palmas de Gran Canaria: Springer, 2011, p. 67-75.
Publisher versionhttp://dx.doi.org/10.1007/978-3-642-21257-4_9
Collections
- IRI - Institut de Robòtica i Informàtica Industrial, CSIC-UPC - Ponències/Comunicacions de congressos [589]
- Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial - Ponències/Comunicacions de congressos [1.513]
- VIS - Visió Artificial i Sistemes Intel·ligents - Ponències/Comunicacions de congressos [296]
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