Eslam Mohammed Abdelkader1,2,*, Osama Moselhi3, Mohamed Marzouk4, Tarek Zayed5
Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 643-661, 2020, DOI: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… More >
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