國立虎尾科技大學 |

Reconstruction and Analysis of 3D Scenes = From Irregularly Distributed 3D Points to Object Classes /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Reconstruction and Analysis of 3D Scenes/ by Martin Weinmann.
其他題名:	From Irregularly Distributed 3D Points to Object Classes /
作者:	Weinmann, Martin.
面頁冊數:	XXII, 233 p. 81 illus., 69 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Optical data processing. -
電子資源:	https://doi.org/10.1007/978-3-319-29246-5
ISBN:	9783319292465

Reconstruction and Analysis of 3D Scenes = From Irregularly Distributed 3D Points to Object Classes /
Weinmann, Martin.

Reconstruction and Analysis of 3D ScenesFrom Irregularly Distributed 3D Points to Object Classes /[electronic resource] :by Martin Weinmann. - 1st ed. 2016. - XXII, 233 p. 81 illus., 69 illus. in color.online resource.

Introduction -- 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.

This 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.

ISBN: 9783319292465

Standard No.: 10.1007/978-3-319-29246-5doiSubjects--Topical Terms:

639187
Optical data processing.

LC Class. No.: TA1630-1650

Dewey Class. No.: 006.6

Reconstruction and Analysis of 3D Scenes = From Irregularly Distributed 3D Points to Object Classes /
LDR:03285nam a22004215i 4500 001 971924
003 DE-He213
005 20200703093321.0
007 cr nn 008mamaa
008 201211s2016 gw | s |||| 0|eng d
020 $a 9783319292465 $9 978-3-319-29246-5
024 7 $a 10.1007/978-3-319-29246-5 $2 doi
035 $a 978-3-319-29246-5
050 4 $a TA1630-1650
072 7 $a UYT $2 bicssc
072 7 $a COM012000 $2 bisacsh
072 7 $a UYT $2 thema
072 7 $a UYQV $2 thema
082 0 4 $a 006.6 $2 23
082 0 4 $a 006.37 $2 23
100 1 $a Weinmann, Martin. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1106574
245 1 0 $a Reconstruction and Analysis of 3D Scenes $h [electronic resource] : $b From Irregularly Distributed 3D Points to Object Classes / $c by Martin Weinmann.
250 $a 1st ed. 2016.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a XXII, 233 p. 81 illus., 69 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a Introduction -- 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 $a This 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.
650 0 $a Optical data processing. $3 639187
650 0 $a Remote sensing. $3 557272
650 0 $a Artificial intelligence. $3 559380
650 1 4 $a Image Processing and Computer Vision. $3 670819
650 2 4 $a Remote Sensing/Photogrammetry. $3 670396
650 2 4 $a Artificial Intelligence. $3 646849
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319292441
776 0 8 $i Printed edition: $z 9783319292458
776 0 8 $i Printed edition: $z 9783319805214
856 4 0 $u https://doi.org/10.1007/978-3-319-29246-5
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)