Laplacian Operator-Based Edge Detectors

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 29; no. 5; pp. 886 - 890
• Main Author: Wang, Xin
• Format: Journal Article
• Language: English
• Published: Los Alamitos, CA IEEE 01.05.2007; IEEE Computer Society; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Application software; Applied sciences; Artificial Intelligence; Biomedical imaging; Computer science; control theory; systems; Computer vision; Derivatives; Detectors; Edge detection; Exact sciences and technology; Image edge detection; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Information Storage and Retrieval - methods; Intelligence; Laplace equations; Laplacian operator; Localization; LoG operator; Low pass filters; Medical robotics; multistage median filter; Nonlinear filters; Operators; Optimization; Pattern analysis; Pattern Recognition, Automated - methods; Pattern recognition. Digital image processing. Computational geometry; Smoothing methods; Multistage; Noise reduction; Second derivative; Modeling; Laplacian; Laplacian operator; multistage median filter; A posteriori estimation; Localization; Pattern analysis; Edge detection; Median filter; Artificial intelligence; LoG operator
• ISSN: 0162-8828; 2160-9292; 1939-3539
• DOI: 10.1109/TPAMI.2007.1027

Laplacian operator is a second derivative operator often used in edge detection. Compared with the first derivative-based edge detectors such as Sobel operator, the Laplacian operator may yield better results in edge localization. Unfortunately, the Laplacian operator is very sensitive to noise. In this paper, based on the Laplacian operator, a model is introduced for making some edge detectors. Also, the optimal threshold is introduced for obtaining a maximum a posteriori (MAP) estimate of edges