Modified whale optimization algorithm for underwater image matching in a UUV vision system
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Modified whale optimization algorithm for underwater image matching in a UUV vision system Zheping Yan 1 & Jinzhong Zhang 1
& Jialing Tang
1
Received: 31 January 2020 / Revised: 6 August 2020 / Accepted: 26 August 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
In this paper, a hybrid whale optimization algorithm based on the Lévy flight strategy (LWOA) and lateral inhibition (LI) is proposed to solve the underwater image matching problem in an unmanned underwater vehicle (UUV) vision system. The proposed image matching technique is called the LI-LWOA. The whale optimization algorithm (WOA) simulates encircling prey, bubble-net attacking and searching for prey to obtain the global optimal solution. The algorithm not only can balance the exploration and exploitation but also has high calculation accuracy. The Lévy flight strategy can expand the search space to avoid premature convergence and enhance the global search ability. In addition, the lateral inhibition mechanism is applied to conduct image preprocessing, which enhances the intensity gradient and image characters, and improves the image matching accuracy. The LI-LWOA achieves the complementary advantages of the LWOA and lateral inhibition to improve the image matching accuracy and enhance the robustness. To verify the overall optimization performance of the LI-LWOA, a series of underwater image matching experiments that seek to maximize the fitness value are performed, and the matching results are compared with those of other algorithms. The experimental results show that the LI-LWOA has better fitness, higher matching accuracy and stronger robustness. In addition, the proposed algorithm is a more effective and feasible method for solving the underwater image matching problem. Keywords Whale optimization algorithm (WOA) . Lévy flight strategy . Lateral inhibition (LI) . Underwater image matching
1 Introduction Image matching is a popular research area in pattern recognition, image analysis, remote sensing and computer vision. The purpose of the image matching is to convert the * Jinzhong Zhang [email protected]
1
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
Multimedia Tools and Applications
template image to a position in the original image and evaluate the match between the template image and the original image by maximizing the similarity measure of the two images. That is, image matching is a technique that determines the small regions of an image that match another image. The existing image matching methods mainly include the following: intensity-based methods and feature-based methods [31, 32]. Intensitybased methods match the position in the original image by moving the template image and maximize the similarity between the matching image and the original image. Feature-based methods use certain features, such as edges, contours, textures, entropy, energy, color and corners, as the basic unit of the image for matching. Computer vision and i
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