A Novel Approach of Edge Detection via a Fast and Adaptive Bidimensional Empirical Mode Decomposition Method

A novel approach of edge detection is proposed that utilizes a bidimensional empirical mode decomposition (BEMD) method as the primary tool. For this purpose, a recently developed fast and adaptive BEMD (FABEMD) is used to decompose the given image into several bidimensional intrinsic mode functions (BIMFs). In FABEMD, order statistics filters (OSFs) are employed to get the upper and lower envelopes in the decomposition process, instead of surface interpolation, which enables fast decomposition and well-characterized BIMFs. Binarization and morphological operations are applied to the first BIMF obtained from FABEMD to achieve the desired edges. The proposed approach is compared with several other edge detection methodologies, which include a combination of classical BEMD and morphological processing, the Canny and Sobel edge detectors, as well as combinations of BEMD/FABEMD and Canny/Sobel edge detectors. Simulation results with real images demonstrate the efficacy and potential of the proposed edge detection algorithm employing FABEMD.