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Journal of Electrical and Computer Engineering
Volume 2016 (2016), Article ID 4125909, 10 pages
http://dx.doi.org/10.1155/2016/4125909
Research Article

Image Edge Detection Based on Gaussian Mixture Model in Nonsubsampled Contourlet Domain

1School of Computer Science and Engineering, Beifang University of Nationalities, Yinchuan 750021, China
2The Institute of Information and System Science, School of Mathematics and Information Science, Beifang University of Nationalities, Yinchuan 750021, China
3Graduate School, Ningxia University, Yinchuan 750021, China

Received 8 January 2016; Revised 31 May 2016; Accepted 29 June 2016

Academic Editor: Panajotis Agathoklis

Copyright © 2016 Li Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.