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Image Edge Detection Based on Gaussian Mixture Model in Nonsubsampled Contourlet Domain
Joint Authors
Source
Journal of Electrical and Computer Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-07-28
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
In order to get accurate location and continuous edges, Gaussian mixture model and local direction modulus nonmaxima suppression are used in high frequency subbands of nonsubsampled Contourlet transform.
The distribution of NSCT high frequency subbands coefficients has the “high spikes, long tail” non-Gaussian statistical characteristic.
Gaussian mixture model (GMM) is used to distinguish the linear singular signal and the nonlinear singular signal on the high frequency subbands.
Local direction modulus nonmaxima suppression is used to refine the linear singular signal.
An appropriate threshold is used to distinguish edge pixels and nonedge pixels to get binary image.
The experimental results demonstrate that the proposed method can capture more continuous edges in multiple directions and has accurate edge location.
And the edges are with great convenience for the image recognition.
American Psychological Association (APA)
Yang, Li& Xia, Chang& Juan, Chang. 2016. Image Edge Detection Based on Gaussian Mixture Model in Nonsubsampled Contourlet Domain. Journal of Electrical and Computer Engineering،Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1108434
Modern Language Association (MLA)
Yang, Li…[et al.]. Image Edge Detection Based on Gaussian Mixture Model in Nonsubsampled Contourlet Domain. Journal of Electrical and Computer Engineering No. 2016 (2016), pp.1-10.
https://search.emarefa.net/detail/BIM-1108434
American Medical Association (AMA)
Yang, Li& Xia, Chang& Juan, Chang. Image Edge Detection Based on Gaussian Mixture Model in Nonsubsampled Contourlet Domain. Journal of Electrical and Computer Engineering. 2016. Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1108434
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1108434