國立虎尾科技大學 |

Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces/ by Pascal Laube.
作者:	Laube, Pascal.
面頁冊數:	XV, 161 p. 56 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-3-658-29017-7
ISBN:	9783658290177

Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces
Laube, Pascal.

Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces[electronic resource] /by Pascal Laube. - 1st ed. 2020. - XV, 161 p. 56 illus.online resource. - Schriftenreihe der Institute für Systemdynamik (ISD) und optische Systeme (IOS),2661-8087. - Schriftenreihe der Institute für Systemdynamik (ISD) und optische Systeme (IOS),.

Machine Learning Methods for Parametrization in Curve and Surface Approximation -- Classification of Geometric Primitives in Point Clouds -- Image Inpainting for High-resolution Textures Using CNN Texture Synthesis.

Pascal Laube presents machine learning approaches for three key problems of reverse engineering of defective structured surfaces: parametrization of curves and surfaces, geometric primitive classification and inpainting of high-resolution textures. The proposed methods aim to improve the reconstruction quality while further automating the process. The contributions demonstrate that machine learning can be a viable part of the CAD reverse engineering pipeline. Contents Machine Learning Methods for Parametrization in Curve and Surface Approximation Classification of Geometric Primitives in Point Clouds Image Inpainting for High-resolution Textures Using CNN Texture Synthesis Target Groups Lecturers and students in the field of machine learning, geometric modeling and information theory Practitioners in the field of machine learning, surface reconstruction and CAD The Author Pascal Laube’s main research interest is the development of machine learning methods for CAD reverse engineering. He is currently developing self-driving cars for an international operating German enterprise in the field of mobility, automotive and industrial technology.

ISBN: 9783658290177

Standard No.: 10.1007/978-3-658-29017-7doiSubjects--Topical Terms:

561253
Machine learning.

LC Class. No.: Q325.5-.7

Dewey Class. No.: 006.31

Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces
LDR:02816nam a22004095i 4500 001 1018534
003 DE-He213
005 20200730120144.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783658290177 $9 978-3-658-29017-7
024 7 $a 10.1007/978-3-658-29017-7 $2 doi
035 $a 978-3-658-29017-7
050 4 $a Q325.5-.7
050 4 $a TK7882.P3
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 006.31 $2 23
100 1 $a Laube, Pascal. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1313549
245 1 0 $a Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces $h [electronic resource] / $c by Pascal Laube.
250 $a 1st ed. 2020.
264 1 $a Wiesbaden : $b Springer Fachmedien Wiesbaden : $b Imprint: Springer Vieweg, $c 2020.
300 $a XV, 161 p. 56 illus. $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
490 1 $a Schriftenreihe der Institute für Systemdynamik (ISD) und optische Systeme (IOS), $x 2661-8087
505 0 $a Machine Learning Methods for Parametrization in Curve and Surface Approximation -- Classification of Geometric Primitives in Point Clouds -- Image Inpainting for High-resolution Textures Using CNN Texture Synthesis.
520 $a Pascal Laube presents machine learning approaches for three key problems of reverse engineering of defective structured surfaces: parametrization of curves and surfaces, geometric primitive classification and inpainting of high-resolution textures. The proposed methods aim to improve the reconstruction quality while further automating the process. The contributions demonstrate that machine learning can be a viable part of the CAD reverse engineering pipeline. Contents Machine Learning Methods for Parametrization in Curve and Surface Approximation Classification of Geometric Primitives in Point Clouds Image Inpainting for High-resolution Textures Using CNN Texture Synthesis Target Groups Lecturers and students in the field of machine learning, geometric modeling and information theory Practitioners in the field of machine learning, surface reconstruction and CAD The Author Pascal Laube’s main research interest is the development of machine learning methods for CAD reverse engineering. He is currently developing self-driving cars for an international operating German enterprise in the field of mobility, automotive and industrial technology.
650 0 $a Machine learning. $3 561253
650 0 $a Computer-aided engineering. $3 560192
650 0 $a Manufactures. $3 680602
650 1 4 $a Machine Learning. $3 1137723
650 2 4 $a Computer-Aided Engineering (CAD, CAE) and Design. $3 669928
650 2 4 $a Manufacturing, Machines, Tools, Processes. $3 1226012
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783658290160
776 0 8 $i Printed edition: $z 9783658290184
830 0 $a Schriftenreihe der Institute für Systemdynamik (ISD) und optische Systeme (IOS), $x 2661-8087 $3 1300463
856 4 0 $u https://doi.org/10.1007/978-3-658-29017-7
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)