Visualization and Visual Analytics Approaches for Image and Video Datasets: A Survey

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
S. Afzal, Sohaib Ghani, M. Hittawe, Sheikh Faisal Rashid, O. Knio, M. Hadwiger, I. Hoteit
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引用次数: 3

Abstract

Image and video data analysis has become an increasingly important research area with applications in different domains such as security surveillance, healthcare, augmented and virtual reality, video and image editing, activity analysis and recognition, synthetic content generation, distance education, telepresence, remote sensing, sports analytics, art, non-photorealistic rendering, search engines, and social media. Recent advances in Artificial Intelligence (AI) and particularly deep learning have sparked new research challenges and led to significant advancements, especially in image and video analysis. These advancements have also resulted in significant research and development in other areas such as visualization and visual analytics, and have created new opportunities for future lines of research. In this survey article, we present the current state of the art at the intersection of visualization and visual analytics, and image and video data analysis. We categorize the visualization articles included in our survey based on different taxonomies used in visualization and visual analytics research. We review these articles in terms of task requirements, tools, datasets, and application areas. We also discuss insights based on our survey results, trends and patterns, the current focus of visualization research, and opportunities for future research.
图像和视频数据集的可视化和可视化分析方法:综述
图像和视频数据分析已经成为一个越来越重要的研究领域,应用于不同的领域,如安全监控、医疗保健、增强现实和虚拟现实、视频和图像编辑、活动分析和识别、合成内容生成、远程教育、远程呈现、遥感、体育分析、艺术、非真实感渲染、搜索引擎和社交媒体。人工智能(AI)特别是深度学习的最新进展引发了新的研究挑战,并导致了重大进步,特别是在图像和视频分析方面。这些进步也导致了可视化和可视化分析等其他领域的重大研究和发展,并为未来的研究领域创造了新的机会。在这篇调查文章中,我们介绍了可视化和视觉分析以及图像和视频数据分析交叉领域的最新技术。我们根据可视化和可视化分析研究中使用的不同分类法对调查中包含的可视化文章进行分类。我们从任务需求、工具、数据集和应用领域的角度来回顾这些文章。我们还讨论了基于我们的调查结果、趋势和模式、当前可视化研究的焦点以及未来研究的机会的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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