Robust Edge Detection Based on Anisotropic Mathematical Morphology and Scale Multiplication in NSCT Domain

Abstract:

A novel edge detection method based on anisotropic mathematical morphology and scale multiplication in nonsubsampled contourlet transform (NSCT) domain is proposed to obtain a superior and robust performance under heavy noise. One preliminary result is obtained using anisotropic morphological gradient of the low-frequency component, yielding a single-pixel response with few pseudo edges. Due to the great ability of NSCT to localize distributed discontinuities such as edges, scale multiplication results of high-frequency components can get rid of a large amount of noise and produce well-localized edge candidates. The final result is a fusion of the detection results of low-frequency component and high-frequency components. Detailed experiments compared with other state-of-the-art methods demonstrate that the proposed method has a superior performance of edge detection and is quite robust even under heavy noise.

• Periodical: Applied Mechanics and Materials (Volume 595)
• Pages: 289-294
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.595.289
• Online since: July 2014
• Authors: Yi Mian Dai*, Yi Quan Wu
• Keywords: Anisotropic Mathematical Morphology, Edge Detection, Image Processing, Nonsubsampled Contourlet Transform (NSCT), Scale Multiplication

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