Partial Differential Equation-Based Enhancement and Crack Detection
Author
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-03-26
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
This paper presents an effective partial differential equation- (PDE-) based preprocessing algorithm for automated image-based crack detection.
The proposed formulation combines various relevant and multiple processes such as contrast and selective edge enhancement in addition to edge-preserving smoothing to enhance the image prior to detection.
The approach is adaptive and controlled by reliable image metrics to determine the stopping time of the PDE ensuring optimum results for various images.
Additionally, a simplified thresholding algorithm based on local global maximum gradient matching is used to extract the crack features from the image.
The proposed scheme does not require arbitrary or manually tuned parameters nor a large dataset for training to obtain good results.
Experiments indicate that the proposed approach performs better when compared to several other algorithms in the literature.
American Psychological Association (APA)
Nnolim, Uche A.. 2019. Partial Differential Equation-Based Enhancement and Crack Detection. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-16.
https://search.emarefa.net/detail/BIM-1197387
Modern Language Association (MLA)
Nnolim, Uche A.. Partial Differential Equation-Based Enhancement and Crack Detection. Mathematical Problems in Engineering No. 2019 (2019), pp.1-16.
https://search.emarefa.net/detail/BIM-1197387
American Medical Association (AMA)
Nnolim, Uche A.. Partial Differential Equation-Based Enhancement and Crack Detection. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-16.
https://search.emarefa.net/detail/BIM-1197387
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1197387