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Edge detection based on fractional order differentiation and its application to railway track images
Author(s) -
Telke Christian,
Beitelschmidt Michael
Publication year - 2015
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.201510325
Subject(s) - edge detection , enhanced data rates for gsm evolution , integer (computer science) , track (disk drive) , computer science , order (exchange) , modular design , image (mathematics) , detector , image processing , artificial intelligence , computer vision , algorithm , telecommunications , finance , operating system , economics , programming language
Edge detection is one of the most fundamental necessities in image processing. Usally, edge detection algorithms are based on integer order differentiation operators. In many applications it is essential to perform a robust edge detection also to noisy input image data with low SNR as well. Thereby, integer based differentiation operators are often not leading to sufficient detection results. For this purpose an edge detector based on fractional order differentiation is introduced, which can significantly improve the detection performance to noisy images. Furthermore, a real application scenario of fractional order based edge detection is given within a modular railway track measurement system. (© 2015 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)