Edge detection via window empirical mode decomposition
Lingfei Liang, Ziliang Ping, Zhonghua Liu · 2012
A novel edge detection of image based on window empirical mode decomposition (WEMD) was proposed in this paper. WEMD was inherited all advantages from EMD and can decompose any nonlinear and non-stationary data into a number of intrinsic mode functions (IMFs). In WEMD, window mean function is employed to get the mean surface in the decomposition process instead of the surface interpolation, which enables fast decomposition. Meanwhile, the drawback of gray spots in IMF images has been avoided. Since the first IMF provides the highest local spatial variations and/or scales of the image, this IMF is then processed for obtaining the edge. 2D-Hilbert transform and non-maxima suppression, hysteresis threshold in Canny method are applied to the first BIMF to achieve the desired edge map. The proposed method is compared with Canny and wavelet edge detection techniques. Simulation results with the real images demonstrate the efficacy of the proposed algorithm for edge detection.