000			03431nam a22003975i 4500
001			85554
003			ES-MaUEC
005			20240111050123.0
007			cr nn 008mamaa
008			160317s2016 gw | s |||| 0|eng d
020			_a9783319292465
040			_aES-MaUEC
050		4	_aTA1560 _b.W456 2016
082	0	4	_a006.6
082	0	4	_a006.37
100	1		_aWeinmann, Martin. _998619 _0Local
245	1	0	_aReconstruction and Analysis of 3D Scenes : _bFrom Irregularly Distributed 3D Points to Object Classes _cby Martin Weinmann
250			_a1st ed.
260			_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XXII, 233 páginas) _b81 ilustraciones, 69 ilustraciones en color
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
505	0		_aIntroduction -- Preliminaries of 3D Point Cloud Processing -- A Brief Survey on 2D and 3D Feature Extraction -- Point Cloud Registration -- Co-Registration of 2D Imagery and 3D Point Cloud Data -- 3D Scene Analysis -- Conclusions and Future Work.
520	3		_aThis unique text/reference presents a detailed review of the processing and analysis of 3D point clouds. A fully automated framework is introduced for the complete processing workflow, incorporating the filtering of noisy data, the extraction of appropriate features, the alignment of 3D point clouds in a common coordinate frame, the enrichment of 3D point cloud data with other types of information, and the semantic interpretation of 3D point clouds. For each of these components, the book describes the theoretical background, and compares the performance of the proposed approaches to that of current state-of-the-art techniques. Topics and features: Reviews techniques for the acquisition of 3D point cloud data and for point quality assessment Explains the fundamental concepts for extracting features from 2D imagery and 3D point cloud data Proposes an original approach to keypoint-based point cloud registration Discusses the enrichment of 3D point clouds by additional information acquired with a thermal camera, and describes a new method for thermal 3D mapping Presents a novel framework for 3D scene analysis, addressing neighborhood selection, feature extraction, feature selection, and classification Covers each aspect of a typical end-to-end processing workflow, from raw 3D point cloud data to semantic objects in the scene This clearly-structured and accessible work will be of great interest to a broad audience, from students at undergraduate or graduate level, to lecturers, practitioners and researchers in photogrammetry, remote sensing, computer vision and robotics.
710	2		_aSpringerLink (Online service) _0Local _9106996
942			_2lcc _cLE
988			_aEBOOK, asignarmaterias , EBSPRINGER
650		7	_aInteligencia artificial _2embne _9413115
650	0	4	_9669495 _aProceso de imágenes
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-29246-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319292465
907			_a.b12948329 _b10-10-17 _c21-11-16
998			_am _a_alco _a_vill _b - - _cm _dz _e- _feng _ggw _h0
945			_aTA1560 .W456 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11591754 _z06-04-17
999			_c85554 _d85554 _x1