@misc{10481/81711,
year = {2022},
month = {9},
url = {https://hdl.handle.net/10481/81711},
abstract = {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.},
publisher = {Journals & Magazines},
keywords = {Evolutionary computation},
keywords = {Image Analysis},
keywords = {Computer vision},
keywords = {Pattern recognition},
keywords = {Image processing},
keywords = {Artificial intelligence},
title = {A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends},
doi = {10.1109/TEVC.2022.3220747},
author = {Bi, Ying and Bing, Xue and Mesejo Santiago, Pablo and Cagnoni, Stefano and Zhang, Mengjie},
}