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Boosted random ferns for object detection

Author
Villamizar, M.A.; Andrade-Cetto, J.; Sanfeliu, A.; Moreno-Noguer, F.
Type of activity
Journal article
Journal
IEEE transactions on pattern analysis and machine intelligence
Date of publication
2018-02-01
Volume
40
Number
2
First page
272
Last page
288
DOI
https://doi.org/10.1109/TPAMI.2017.2676778 Open in new window
Project funding
Aerial robotic system integrating multiple arms and advanced manipulation capabilities for inspection and maintenance
Robot-human collaboration for transporting goods in urban areas
TIN2014-58178-R Instructing robots using natural communication skills National Project
Tight integration of EGNSS and on-board sensors for port vehicle automation
Repository
http://hdl.handle.net/2117/116312 Open in new window
URL
http://ieeexplore.ieee.org/document/7867865/ Open in new window
Abstract
In this paper we introduce the Boosted Random Ferns (BRFs) to rapidly build discriminative classifiers for learning and detecting object categories. At the core of our approach we use standard random ferns, but we introduce four main innovations that let us bring ferns from an instance to a category level, and still retain efficiency. First, we define binary features on the histogram of oriented gradients-domain (as opposed to intensity-), allowing for a better representation of intra-class vari...
Citation
Villamizar, M.A., Andrade-Cetto, J., Sanfeliu, A., Moreno-Noguer, F. Boosted Random ferns for object detection. "IEEE transactions on pattern analysis and machine intelligence", 1 Febrer 2018, vol. 40, núm. 2, p. 272-288.
Keywords
Image processing and computer vision, boosting, object detection, online-boosting, random ferns
Group of research
ROBiri - IRI Robotics Group
VIS - Artificial Vision and Intelligent Systems