國立虎尾科技大學 |

Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging = Mathematical Imaging and Vision /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging/ edited by Ke Chen, Carola-Bibiane Schönlieb, Xue-Cheng Tai, Laurent Younces.
Reminder of title:	Mathematical Imaging and Vision /
other author:	Younces, Laurent.
Description:	20 illus., 10 illus. in color.online resource. :
Contained By:	Springer Nature Living Reference
Subject:	Mathematical Models of Cognitive Processes and Neural Networks. -
Online resource:	https://doi.org/10.1007/978-3-030-03009-4
ISBN:	9783030030094

Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging = Mathematical Imaging and Vision /
Handbook of Mathematical Models and Algorithms in Computer Vision and ImagingMathematical Imaging and Vision /[electronic resource] :edited by Ke Chen, Carola-Bibiane Schönlieb, Xue-Cheng Tai, Laurent Younces. - 20 illus., 10 illus. in color.online resource.

A latest update on the state of arts research developments in the fast growing and highly multidisciplinary field of Variational Methods and Effective Algorithms for Imaging and Vision. The emphasis is on the Variational Methods which represent the optimal solutions to class of imaging and vision problems and on Effective Algorithms which are necessary for the methods to be translate to practical use in various applications. Viewing discrete images as data sampled from functional surfaces enable the use of Advanced tools from Calculus, Functions and Calculus of Variations, Optimization and provide the basis of high resolution imaging through variational models. No other frameworks can provide the comparable accuracy and precision to Imaging and Vision. Ample references are given on topics covered. All chapters will have introductions so that the book is accessible to graduate students. For new comers to the field, the book provides a comprehensive and fast track introduction to the c to save time and get on with tackling new and emerging challenges, rather than running the risk of reproducing / comparing to some old works already done or reinventing same results. For researchers, exposure to the state of arts of research works leads to an overall view of the entire field so as to guide new research directions and avoid pitfalls in moving the field forward and looking into the next 25 years of imaging and information sciences. Primary audience: Graduate students, Researchers, Imaging and vision practitioners, Applied mathematicians, Medical Imagers, Engineers, and Computer scientists.

ISBN: 9783030030094

Standard No.: 10.1007/978-3-030-03009-4doiSubjects--Topical Terms:

884110
Mathematical Models of Cognitive Processes and Neural Networks.

LC Class. No.: QA71-90

Dewey Class. No.: 518

Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging = Mathematical Imaging and Vision /
LDR:02939nam a22003495i 4500 001 1051265
003 DE-He213
005 20210702130925.0
007 cr nn 008mamaa
008 220103s2021 gw | s |||| 0|eng d
020 $a 9783030030094 $9 978-3-030-03009-4
024 7 $a 10.1007/978-3-030-03009-4 $2 doi
035 $a 978-3-030-03009-4
050 4 $a QA71-90
072 7 $a PBKS $2 bicssc
072 7 $a MAT006000 $2 bisacsh
072 7 $a PBKS $2 thema
082 0 4 $a 518 $2 23
245 1 0 $a Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging $h [electronic resource] : $b Mathematical Imaging and Vision / $c edited by Ke Chen, Carola-Bibiane Schönlieb, Xue-Cheng Tai, Laurent Younces.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a 20 illus., 10 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
520 $a A latest update on the state of arts research developments in the fast growing and highly multidisciplinary field of Variational Methods and Effective Algorithms for Imaging and Vision. The emphasis is on the Variational Methods which represent the optimal solutions to class of imaging and vision problems and on Effective Algorithms which are necessary for the methods to be translate to practical use in various applications. Viewing discrete images as data sampled from functional surfaces enable the use of Advanced tools from Calculus, Functions and Calculus of Variations, Optimization and provide the basis of high resolution imaging through variational models. No other frameworks can provide the comparable accuracy and precision to Imaging and Vision. Ample references are given on topics covered. All chapters will have introductions so that the book is accessible to graduate students. For new comers to the field, the book provides a comprehensive and fast track introduction to the c to save time and get on with tackling new and emerging challenges, rather than running the risk of reproducing / comparing to some old works already done or reinventing same results. For researchers, exposure to the state of arts of research works leads to an overall view of the entire field so as to guide new research directions and avoid pitfalls in moving the field forward and looking into the next 25 years of imaging and information sciences. Primary audience: Graduate students, Researchers, Imaging and vision practitioners, Applied mathematicians, Medical Imagers, Engineers, and Computer scientists.
650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 884110
650 2 4 $a Partial Differential Equations. $3 671119
650 2 4 $a Optimization. $3 669174
650 2 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 671334
650 1 4 $a Computational Mathematics and Numerical Analysis. $3 669338
650 0 $a Neural networks (Computer science) . $3 1253765
650 0 $a Partial differential equations. $3 1102982
650 0 $a Mathematical optimization. $3 527675
650 0 $a Optical data processing. $3 639187
650 0 $a Computer mathematics. $3 1199796
700 1 $a Younces, Laurent. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1355759
700 1 $a Tai, Xue-Cheng. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 898486
700 1 $a Schönlieb, Carola-Bibiane. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1355758
700 1 $a Chen, Ke. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 675465
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature Living Reference
856 4 0 $u https://doi.org/10.1007/978-3-030-03009-4
912 $a ZDB-2-SMA
912 $a ZDB-2-SXRC
912 $a ZDB-2-SLR
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Reference Module Computer Science and Engineering (SpringerNature-43748)