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Improved edge detection based on fractional derivatives for real‐time measurement systems
Author(s) -
Loderer Maximilian,
Beitelschmidt Michael
Publication year - 2019
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.201900439
Subject(s) - prewitt operator , sobel operator , filter (signal processing) , artificial intelligence , computer science , edge detection , computer vision , enhanced data rates for gsm evolution , segmentation , pattern recognition (psychology) , image (mathematics) , image processing
Abstract Since edges are one of the most commonly used recognition features in object detection and image segmentation, their robust and clear recognition as well as the calculation in real time is important for most applications. It could be shown that the fractional filter CRONE leads to significantly better results in noisy images than classical filters such as Sobel or Prewitt, especially under the condition of real‐time capability. Unfortunately, the CRONE filter is a one‐dimensional filter and only leads to good results if its filter direction is perpendicular to the edge. Therefore, this paper presents the novel approach CRONE2D, the extension of the CRONE for surface input data like images, with much better results in detection of arbitrarily oriented edges.