Open Access
ARTICLE
A Multi-objective Invasive Weed Optimization Method for Segmentation of Distress Images
Eslam Mohammed Abdelkader1,2,*, Osama Moselhi3, Mohamed Marzouk4, Tarek Zayed5
1 Ph.D. candidate, Department of Building, Civil, and Environmental Engineering, Concordia University, Montreal, QC, Canada.
2 Assistant lecturer, Structural Engineering Department, Faculty of Engineering, Cairo University, Egypt.
3 Professor, Department of Building, Civil, and Environmental Engineering, Concordia University, Montreal, QC, Canada.
4 Professor, Structural Engineering Department, Faculty of Engineering, Cairo University, Egypt.
5 Professor, Department of Building and Real Estate, the Hong Kong Polytechnic University, Hung Hom, Hong Kong.
* Corresponding Author: Eslam Mohammed Abdelkader,
Intelligent Automation & Soft Computing 2020, 26(4), 643-661. https://doi.org/10.32604/iasc.2020.010100
Abstract
Image segmentation is one of the fundamental stages in computer vision
applications. Several meta-heuristics have been applied to solve the
segmentation problems by extending the Otsu and entropy functions. However,
no single-objective function can optimally handle the diversity of information in
images besides the multimodality issues of gray-level images. This paper
presents a self-adaptive multi-objective optimization-based method for the
detection of crack images in reinforced concrete bridges. The proposed method
combines the flexibility of information theory functions in addition to the
invasive weed optimization algorithm for bi-level thresholding. The capabilities
of the proposed method are demonstrated through comparisons with singleobjective optimization-based methods, conventional segmentation methods,
multi-objective genetic algorithm-based method, multi-objective particle swarmbased method and multi-objective harmony search-based method. The
proposed method outperformed the previously-mentioned segmentation
methods, whereas the average values of mean-squared error, peak signal to
noise ratio and structural similarity index are equal to 0.0784, 11.4831 and
0.9921, respectively.
Keywords
Cite This Article
E. Mohammed Abdelkader, O. Moselhi, M. Marzouk and T. Zayed, "A multi-objective invasive weed optimization method for segmentation of distress images,"
Intelligent Automation & Soft Computing, vol. 26, no.4, pp. 643–661, 2020.
Citations