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3D Point Cloud Analysis : Traditional, Deep Learning, and Explainable Machine Learning Methods / by Shan Liu, Min Zhang, Pranav Kadam, C.-C. Jay Kuo
版 | 1st ed. 2021. |
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出版者 | Cham : Springer International Publishing : Imprint: Springer |
出版年 | 2021 |
本文言語 | 英語 |
大きさ | XIV, 146 p. 92 illus., 88 illus. in color : online resource |
著者標目 | *Liu, Shan author Zhang, Min author Kadam, Pranav author Kuo, C.-C. Jay author SpringerLink (Online service) |
件 名 | LCSH:Machine learning LCSH:Artificial intelligence LCSH:Pattern recognition systems LCSH:Image processing -- Digital techniques 全ての件名で検索 LCSH:Computer vision FREE:Machine Learning FREE:Artificial Intelligence FREE:Automated Pattern Recognition FREE:Computer Imaging, Vision, Pattern Recognition and Graphics FREE:Computer Vision |
一般注記 | I. Introduction -- II. Traditional point cloud analysis -- III. Deep-learning-based point cloud analysis -- IV. Explainable machine learning methods for point cloud analysis -- V. Conclusion and future work This book introduces the point cloud; its applications in industry, and the most frequently used datasets. It mainly focuses on three computer vision tasks -- point cloud classification, segmentation, and registration -- which are fundamental to any point cloud-based system. An overview of traditional point cloud processing methods helps readers build background knowledge quickly, while the deep learning on point clouds methods include comprehensive analysis of the breakthroughs from the past few years. Brand-new explainable machine learning methods for point cloud learning, which are lightweight and easy to train, are then thoroughly introduced. Quantitative and qualitative performance evaluations are provided. The comparison and analysis between the three types of methods are given to help readers have a deeper understanding. With the rich deep learning literature in 2D vision, a natural inclination for 3D vision researchers is to develop deep learning methods for point cloudprocessing. Deep learning on point clouds has gained popularity since 2017, and the number of conference papers in this area continue to increase. Unlike 2D images, point clouds do not have a specific order, which makes point cloud processing by deep learning quite challenging. In addition, due to the geometric nature of point clouds, traditional methods are still widely used in industry. Therefore, this book aims to make readers familiar with this area by providing comprehensive overview of the traditional methods and the state-of-the-art deep learning methods. A major portion of this book focuses on explainable machine learning as a different approach to deep learning. The explainable machine learning methods offer a series of advantages over traditional methods and deep learning methods. This is a main highlight and novelty of the book. By tackling three research tasks -- 3D object recognition, segmentation, and registration using our methodology -- readers will have a sense of how to solve problems in a different way and can apply the frameworks to other 3D computer vision tasks, thus give them inspiration for their own future research. Numerous experiments, analysis and comparisons on three 3D computer vision tasks (object recognition, segmentation, detection and registration) are provided so that readers can learn how to solve difficult Computer Vision problems HTTP:URL=https://doi.org/10.1007/978-3-030-89180-0 |
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電子ブック | 配架場所 | 資料種別 | 巻 次 | 請求記号 | 状 態 | 予約 | コメント | ISBN | 刷 年 | 利用注記 | 指定図書 | 登録番号 |
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電子ブック | オンライン | 電子ブック |
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Springer eBooks | 9783030891800 |
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電子リソース |
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EB00229231 |