@Article{iasc.2020.010100,
AUTHOR = {Eslam Mohammed Abdelkader, Osama Moselhi, Mohamed Marzouk, Tarek Zayed},
TITLE = {A Multi-objective Invasive Weed Optimization Method for Segmentation  of Distress Images},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {4},
PAGES = {643--661},
URL = {http://www.techscience.com/iasc/v26n4/40270},
ISSN = {2326-005X},
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.},
DOI = {10.32604/iasc.2020.010100}
}