Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 010552 Bucharest, Romania.
Sensors (Basel). 2023 Jan 14;23(2):967. doi: 10.3390/s23020967.
Image registration is one of the most important image processing tools enabling recognition, classification, detection and other analysis tasks. Registration methods are used to solve a large variety of real-world problems, including remote sensing, computer vision, geophysics, medical image analysis, surveillance, and so on. In the last few years, nature-inspired algorithms and metaheuristics have been successfully used to address the image registration problem, becoming a solid alternative for direct optimization methods. The aim of this paper is to investigate and summarize a series of state-of-the-art works reporting evolutionary-based registration methods. The papers were selected using the PRISMA 2020 method. The reported algorithms are reviewed and compared in terms of evolutionary components, fitness function, image similarity measures and algorithm accuracy indexes used in the alignment process.
图像配准是最重要的图像处理工具之一，可实现识别、分类、检测和其他分析任务。配准方法被用于解决各种实际问题，包括遥感、计算机视觉、地球物理学、医学图像分析、监控等。在过去几年中，受自然启发的算法和元启发式算法已成功用于解决图像配准问题，成为直接优化方法的有力替代方案。本文旨在调查和总结一系列基于进化的配准方法的最新研究工作。使用 PRISMA 2020 方法选择了这些论文。报告的算法根据进化组件、适应度函数、图像相似性度量和对齐过程中使用的算法精度指标进行了回顾和比较。
