Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/42186
Title: Ridge–based curvilinear structure detection for identifying road in remote sensing image and backbone in neuron dendrite image
Authors: Kong, Fanqiang
Govindaraj, Vishnu Varthanan
Zhang, Yu-Dong
First Published: 27-Apr-2018
Publisher: Springer Verlag (Germany)
Citation: Multimedia Tools and Applications, 2018
Abstract: The curvilinear structure detection is widely applied in many real tasks, such as the fiber classification, river finding, blood vessel detection, and so on. In this paper, we proposed to use the ridge-based curvilinear structure detection (RCSD) for the road extraction from the remote sensing images. First, we employed the morphology trivial opening operation to filter out almost all the small clusters of noise and the small paths. Then RCSD was used to find the road from the remote sensing images. The experiments showed that our proposed method is efficient and give better results than the current existing road-detection methods. Considering the similar structure between backbone in the neuron dendrite images and the road in remote sensing images, we extended the application of RCSD to the backbone detection in neuron dendrite images. The results on backbone detection also proved the efficiency of RCSD.
DOI Link: 10.1007/s11042-018-5976-7
ISSN: 1380-7501
eISSN: 1573-7721
Links: https://link.springer.com/article/10.1007%2Fs11042-018-5976-7
http://hdl.handle.net/2381/42186
Embargo on file until: 29-Apr-2019
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2018, Springer Verlag (Germany). Deposited with reference to the publisher’s open access archiving policy. (http://www.rioxx.net/licenses/all-rights-reserved)
Description: The file associated with this record is under embargo until 12 months after publication, in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above.
Appears in Collections:Published Articles, Dept. of Computer Science

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