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3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods [electronic resource]
서명 / 저자 3D Point Cloud Analysis [electronic resource] : Traditional, Deep Learning, and Explainable Machine Learning Methods / by Shan Liu, Min Zhang, Pranav Kadam, C.-C. Jay Kuo.
저자명 Liu, Shan. author. aut http://id.loc.gov/vocabulary/relators/aut
Zhang, Min. author. aut http://id.loc.gov/vocabulary/relators/aut ; Kadam, Pranav. author. aut http://id.loc.gov/vocabulary/relators/aut ; Kuo, C.-C. Jay. author. aut http://id.loc.gov/vocabulary/relators/aut
단체명 SpringerLink (Online service)
발행사항 Cham : Springer International Publishing : Imprint: Springer, 2021.
Online Access https://doi.org/10.1007/978-3-030-89180-0 URL

서지기타정보

서지기타정보
청구기호 Q325.5-.7
판사항 1st ed. 2021.
형태사항 XIV, 146 p. 92 illus., 88 illus. in color. online resource.
언어 English
내용 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.
주제 Machine learning.
Artificial intelligence.
Pattern recognition systems.
Image processing—Digital techniques.
Computer vision.
Machine Learning.
Artificial Intelligence.
Automated Pattern Recognition.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Computer Vision.
보유판 및 특별호 저록 Springer Nature eBook
Printed edition: 9783030891794 Printed edition: 9783030891817 Printed edition: 9783030891824
ISBN 9783030891800
기타 표준번호 10.1007/978-3-030-89180-0
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