000			01862cam a2200445Ii 4500
003			OCoLC
005			20240807102501.0
008			211214s2021 sz a ob 001 0 eng d
020			_a9783030891800
020			_a3030891801
020			_z9783030891794
020			_z3030891798
041			_aEnglish
049			_aMAIN
072		7	_aUYQM _2bicssc
072		7	_aCOM004000 _2bisacsh
072		7	_aUYQM _2thema
082	0	4	_a006.37 _223 _bLIU
100	1		_aLiu, Shan,
100	1		_eauthor.
245	1	0	_a3D point cloud analysis : _btraditional, deep learning, and explainable machine learning methods / _cShan Liu, Min Zhang, Pranav Kadam, C.-C. Jay Kuo.
260			_aCham : _bSpringer International Publishing AG, _c2021.
300			_a1 online resource : _billustrations (chiefly color)
504			_aIncludes bibliographical references and index.
505			_aI. 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.
520			_aThe 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 cloud processing.
650		0	_aComputer vision.
650		0	_aPattern perception.
650		0	_aMachine learning.
700	1		_aZhang, Min,
700	1		_aKadam, Pranav,
700	1		_aKuo, C.-C. Jay
700	1		_eauthor.
700	1		_eauthor.
700	1		_q(Chung-Chieh Jay), _eauthor.
856	4	0	_uhttps://ezproxy.lib.gla.ac.uk/login?url=https://link.springer.com/10.1007/978-3-030-89180-0
856	4	0	_zConnect to e-book
907			_a.b3846472x
942			_2ddc _cBOOKS
999			_c43334 _d43334