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Optical Engineering • Open Access

Invariant Hough random ferns for RGB-D-based object detection
Author(s): Xiaoping Lou; Mingli Dong; Jun Wang; Peng Sun; Yimin Lin

Paper Abstract

This paper studies the challenging problem of object detection using rich image and depth features. An invariant Hough random ferns framework for RGB-D images is proposed here, which primarily consists of a rotation-invariant RGB-D local binary feature, random ferns classifier training, Hough mapping and voting, searches for the maxima, and back projection. In comparison with traditional three-dimensional local feature extraction techniques, this method is effective in reducing the amount of computation required for feature extraction and matching. Moreover, the detection results showed that the proposed method is robust against rotation and scale variations, changes in illumination, and part-occlusions. The authors believe that this method will facilitate the use of perception in fields such as robotics.

Paper Details

Date Published: 11 March 2016
PDF: 7 pages
Opt. Eng. 55(9) 091403 doi: 10.1117/1.OE.55.9.091403
Published in: Optical Engineering Volume 55, Issue 9
Show Author Affiliations
Xiaoping Lou, Beijing Information Science & Technology Univ. (China)
Mingli Dong, Beijing Information Science & Technology University (China)
Beijing Information Science & Technology University (China)
Jun Wang, Beijing Information Science & Technology University (China)
Beijing Information Science & Technology University (China)
Peng Sun, Beijing Information Science & Technology University (China)
Beijing Information Science & Technology University (China)
Yimin Lin, Beijing Information Science & Technology University (China)
Beijing Information Science & Technology University (China)


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