A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends Bi, Ying Bing, Xue Mesejo Santiago, Pablo Cagnoni, Stefano Zhang, Mengjie Evolutionary computation Image Analysis Computer vision Pattern recognition Image processing Artificial intelligence Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, imagerelated tasks are very challenging due to many factors, e.g., high variations across images, high dimensionality, domain expertise requirement, and image distortions. Evolutionary computation (EC) approaches have been widely used for image analysis with significant achievement. However, there is no comprehensive survey of existing EC approaches to image analysis. To fill this gap, this paper provides a comprehensive survey covering all essential EC approaches to important image analysis tasks including edge detection, image segmentation, image feature analysis, image classification, object detection, and others. This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis. The applications, challenges, issues, and trends associated to this research field are also discussed and summarised to provide further guidelines and opportunities for future research. 2023-05-22T10:32:39Z 2023-05-22T10:32:39Z 2022-09-14 info:eu-repo/semantics/article Published by: Y. Bi, B. Xue, P. Mesejo, S. Cagnoni and M. Zhang, "A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends," in IEEE Transactions on Evolutionary Computation, vol. 27, no. 1, pp. 5-25, Feb. 2023, doi: 10.1109/TEVC.2022.3220747. https://hdl.handle.net/10481/81711 10.1109/TEVC.2022.3220747 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivatives 4.0 Internacional Journals & Magazines