Edge Detection from RGB-D Image Based on Structured Forests
Joint Authors
Liu, Yanli
Zhang, Heng
Wen, Zhenqiang
Xu, Gang
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-07-14
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
This paper looks into the fundamental problem in computer vision: edge detection.
We propose a new edge detector using structured random forests as the classifier, which can make full use of RGB-D image information from Kinect.
Before classification, the adaptive bilateral filter is used for the denoising processing of the depth image.
As data sources, information of 13 channels from RGB-D image is computed.
In order to train the random forest classifier, the approximation measurement of the information gain is used.
All the structured labels at a given node are mapped to a discrete set of labels using the Principal Component Analysis (PCA) method.
NYUD2 dataset is used to train our structured random forests.
The random forest algorithm is used to classify the RGB-D image information for extracting the edge of the image.
In addition to the proposed methodology, the quantitative comparisons of different algorithms are presented.
The results of the experiments demonstrate the significant improvements of our algorithm over the state of the art.
American Psychological Association (APA)
Zhang, Heng& Wen, Zhenqiang& Liu, Yanli& Xu, Gang. 2016. Edge Detection from RGB-D Image Based on Structured Forests. Journal of Sensors،Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1110501
Modern Language Association (MLA)
Zhang, Heng…[et al.]. Edge Detection from RGB-D Image Based on Structured Forests. Journal of Sensors No. 2016 (2016), pp.1-10.
https://search.emarefa.net/detail/BIM-1110501
American Medical Association (AMA)
Zhang, Heng& Wen, Zhenqiang& Liu, Yanli& Xu, Gang. Edge Detection from RGB-D Image Based on Structured Forests. Journal of Sensors. 2016. Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1110501
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1110501