Noise-robust color edge detection using anisotropic morphological directional derivative matrix

• Published in: Signal processing Vol. 165; pp. 90 - 103
• Main Authors: Li, Ou, Shui, Peng-Lang
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2019
• Subjects: Anisotropic morphological directional derivative matrix; Candidate edge map; Color edge detector; Groundtruth of edges; Robust to impulsive noise; Color edge detector; Robust to impulsive noise; Candidate edge map; Anisotropic morphological directional derivative matrix; Groundtruth of edges
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2019.06.036

•A noise-robust color edge detector using anisotropic morphological directional derivative matrix is proposed.•A database of fifty color images with ground truth is constructed.•The proposed detector shows great robustness against both Gaussian and impulsive noise and it is compared with several detectors on the database. In this paper, a color edge detector using the anisotropic morphological directional derivatives (AMDDs) is presented to detect edges in color images corrupted by Gaussian or impulsive noise. The AMDD matrix, robust to impulsive noise owing to the underlying morphological operators, is constructed to represent edge information at each pixel of a color image. The color edge strength map and color edge direction map of a color image are extracted by spatial and directional matched filtering and singular value decomposition of the AMDD matrices. Embedding them in the route of the Canny detector yields a noise-robust color edge detector. Moreover, a color image database with groundtruths (GTs) of edges are built. The GT of a color image is generated in three steps. First, the contours and results of multiple color edge detectors are fused into a candidate edge map (CEM). Next, the CEM, the original image, and a special software for edge modification are sent to twenty experienced observers to modify the CEM. Finally, their results are used to yield the edge pixels, non-edge pixels, and don't care regions in the GT by the voting rule. The proposed detector is compared with existing color edge detectors on the database.