Open AccessGeometrical flow‐guided fast beamlet transform for crack detection
Author(s) -
Lin Zhe,
Zhao Xiaohua
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.0747
Subject(s) - computation , process (computing) , computer science , image (mathematics) , computer vision , artificial intelligence , line (geometry) , algorithm , flow (mathematics) , speedup , mathematics , geometry , parallel computing , operating system
Beamlet transform has been widely used for extracting line features from images, which is an excellent multiscale geometric analysis method. However, it has a major drawback that it always performs too slowly due to very much redundant computation. In many application fields, the speed of the original beamlet transform is almost unbearable. To cure the problem, beamlet transform is improved by introducing geometrical flow, which utilises image semantic information in the process of generating beamlets. Besides, to further speed up the algorithm, interesting factor is presented to reduce recursively partitioned boxes. As a result, lots of computation time is saved. Experiments are conducted on various crack images and the results show that the proposed method runs significantly faster than the original beamlet transform. Cracks in an image are detected accurately. Moreover, the proposed method is robustly enough since the performance is hardly affected by crack shape and background texture.