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001441319 001__ 1441319
001441319 003__ OCoLC
001441319 005__ 20230309004732.0
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001441319 019__ $$a1288560491$$a1288631935$$a1288670239$$a1289371327$$a1289539142$$a1294367994
001441319 020__ $$a9783030891800$$q(electronic bk.)
001441319 020__ $$a3030891801$$q(electronic bk.)
001441319 020__ $$z9783030891794
001441319 020__ $$z3030891798
001441319 0247_ $$a10.1007/978-3-030-89180-0$$2doi
001441319 035__ $$aSP(OCoLC)1288465335
001441319 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dGW5XE$$dFIE$$dEBLCP$$dOCLCF$$dOCLCO$$dDCT$$dOCLCQ$$dOCLCO$$dWAU$$dUKAHL$$dOCLCQ
001441319 049__ $$aISEA
001441319 050_4 $$aTA1634$$b.L58 2021
001441319 08204 $$a006.3/7$$223
001441319 1001_ $$aLiu, Shan,$$eauthor.
001441319 24510 $$a3D point cloud analysis :$$btraditional, deep learning, and explainable machine learning methods /$$cShan Liu, Min Zhang, Pranav Kadam, C.-C. Jay Kuo.
001441319 264_1 $$aCham :$$bSpringer,$$c[2021]
001441319 264_4 $$c©2021
001441319 300__ $$a1 online resource (xiv, 146 pages) :$$billustrations (chiefly color)
001441319 336__ $$atext$$btxt$$2rdacontent
001441319 337__ $$acomputer$$bc$$2rdamedia
001441319 338__ $$aonline resource$$bcr$$2rdacarrier
001441319 347__ $$atext file
001441319 347__ $$bPDF
001441319 504__ $$aIncludes bibliographical references and index.
001441319 5050_ $$aIntroduction -- Traditional point cloud analysis -- Deep-learning-based point cloud analysis -- Explainable machine learning methods for point cloud analysis -- Conclusion and future work.
001441319 506__ $$aAccess limited to authorized users.
001441319 520__ $$aThis 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 cloud processing. 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.
001441319 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed December 21, 2021).
001441319 650_0 $$aComputer vision.
001441319 650_0 $$aPattern perception.
001441319 650_0 $$aMachine learning.
001441319 650_6 $$aVision par ordinateur.
001441319 650_6 $$aPerception des structures.
001441319 650_6 $$aApprentissage automatique.
001441319 655_0 $$aElectronic books.
001441319 7001_ $$aZhang, Min,$$eauthor.
001441319 7001_ $$aKadam, Pranav,$$eauthor.
001441319 7001_ $$aKuo, C.-C. Jay$$q(Chung-Chieh Jay),$$eauthor.
001441319 77608 $$iPrint version:$$aLiu, Shan.$$t3D point cloud analysis.$$dCham : Springer, [2021]$$z3030891798$$z9783030891794$$w(OCoLC)1268111940
001441319 852__ $$bebk
001441319 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-89180-0$$zOnline Access$$91397441.1
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001441319 980__ $$aBIB
001441319 980__ $$aEBOOK
001441319 982__ $$aEbook
001441319 983__ $$aOnline
001441319 994__ $$a92$$bISE