Modified water wave optimization algorithm for underwater multilevel thresholding image segmentation
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Modified water wave optimization algorithm for underwater multilevel thresholding image segmentation Zheping Yan 1 & Jinzhong Zhang 1
& Jialing Tang
1
Received: 27 November 2019 / Revised: 15 July 2020 / Accepted: 18 August 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Multilevel thresholding is a simple and important method for image segmentation in various applications that has drawn widespread attention in recent years. However, the computational complexity increases correspondingly when the threshold levels increase. To overcome this drawback, a modified water wave optimization (MWWO) algorithm with the elite opposition-based learning strategy and the ranking-based mutation operator for underwater image segmentation is proposed in this paper. The elite opposition-based learning strategy increases the diversity of the population and prevents the search from stagnating to improve the calculation accuracy. The ranking-based mutation operator increases the selection probability. MWWO can effectively balance exploration and exploitation to obtain the optimal solution in the search space. To objectively evaluate the overall performance of the proposed algorithm, MWWO is compared with six state-of-the-art meta-heuristic algorithms by maximizing the fitness value of Kapur’s entropy method to obtain the optimal threshold through experiments on ten test images. The fitness value, the best threshold values, the execution time, the peak signal to noise ratio (PSNR), the structure similarity index (SSIM), and the Wilcoxon’s rank-sum test are used as important metrics to evaluate the segmentation effect of underwater images. The experimental results show that MWWO has a better segmentation effect and stronger robustness compared with other algorithms and an effective and feasible method for solving underwater multilevel thresholding image segmentation. Keywords Multilevel thresholding . Image segmentation . Water wave optimization . Elite opposition-based learning strategy . Ranking-based mutation operator . Kapur’s entropy
* Jinzhong Zhang [email protected]
1
College of Automation, Harbin Engineering University, Harbin 150001, China
Multimedia Tools and Applications
1 Introduction Unmanned underwater vehicles (UUVs) with vision systems not only have the ability to acquire optical images and video information, but they are also able to perform image and video information processing, feature extraction and classification recognition. The mission of a UUV vision system is to quickly and accurately obtain information about underwater targets, then process the obtained information in real time, feed back the processing results to a computer network, and finally guide the UUV to perform the correct operation [17, 19, 23, 24, 30]. The three-dimensional model of a UUV equipped with a vision system is given in Fig. 1. Image segmentation is a crucial and basic process that divides a given image into several distinct regions and extracts the target object of interest from the comple
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