東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Applications of evolutionary computa...

Cuevas, Erik.

Linked to FindBook

Google Book

Amazon

博客來

Applications of evolutionary computation in image processing and pattern recognition

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applications of evolutionary computation in image processing and pattern recognition/ by Erik Cuevas, Daniel Zaldivar, Marco Perez-Cisneros.
Author:	Cuevas, Erik.
other author:	Zaldivar, Daniel.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	xv, 274 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Image Segmentation Based on Differential Evolution Optimization -- Motion Estimation Based on Artificial Bee Colony (ABC) -- Ellipse Detection on Images Inspired by the Collective Animal Behavior -- Template Matching by Using the States of Matter Algorithm -- Estimation of Multiple View Relations Considering Evolutionary Approaches -- Circle Detection on Images Based on an Evolutionary Algorithm that Reduces the Number of Function Evaluations -- Otsu and Kapur Segmentation Based on Harmony Search Optimization -- Leukocyte Detection by Using Electromagnetism-Like Optimization -- Automatic Segmentation by Using an Algorithm Based on the Behavior of Locust Swarms.
Contained By:	Springer eBooks
Subject:	Evolutionary computation. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-26462-2
ISBN:	9783319264622$q(electronic bk.)

Applications of evolutionary computation in image processing and pattern recognition
Cuevas, Erik.

Applications of evolutionary computation in image processing and pattern recognition[electronic resource] /by Erik Cuevas, Daniel Zaldivar, Marco Perez-Cisneros. - Cham :Springer International Publishing :2016. - xv, 274 p. :ill., digital ;24 cm. - Intelligent systems reference library,v.1001868-4394 ;. - Intelligent systems reference library ;v.24..

Introduction -- Image Segmentation Based on Differential Evolution Optimization -- Motion Estimation Based on Artificial Bee Colony (ABC) -- Ellipse Detection on Images Inspired by the Collective Animal Behavior -- Template Matching by Using the States of Matter Algorithm -- Estimation of Multiple View Relations Considering Evolutionary Approaches -- Circle Detection on Images Based on an Evolutionary Algorithm that Reduces the Number of Function Evaluations -- Otsu and Kapur Segmentation Based on Harmony Search Optimization -- Leukocyte Detection by Using Electromagnetism-Like Optimization -- Automatic Segmentation by Using an Algorithm Based on the Behavior of Locust Swarms.

This book presents the use of efficient Evolutionary Computation (EC) algorithms for solving diverse real-world image processing and pattern recognition problems. It provides an overview of the different aspects of evolutionary methods in order to enable the reader in reaching a global understanding of the field and, in conducting studies on specific evolutionary techniques that are related to applications in image processing and pattern recognition. It explains the basic ideas of the proposed applications in a way that can also be understood by readers outside of the field. Image processing and pattern recognition practitioners who are not evolutionary computation researchers will appreciate the discussed techniques beyond simple theoretical tools since they have been adapted to solve significant problems that commonly arise on such areas. On the other hand, members of the evolutionary computation community can learn the way in which image processing and pattern recognition problems can be translated into an optimization task. The book has been structured so that each chapter can be read independently from the others. It can serve as reference book for students and researchers with basic knowledge in image processing and EC methods.

ISBN: 9783319264622$q(electronic bk.)

Standard No.: 10.1007/978-3-319-26462-2doiSubjects--Topical Terms:

582189
Evolutionary computation.

LC Class. No.: TA347.E96

Dewey Class. No.: 006.3823

Applications of evolutionary computation in image processing and pattern recognition
LDR:03003nmm a2200325 a 4500 001 2029187
003 DE-He213
005 20160722131516.0
006 m d
007 cr nn 008maaau
008 160908s2016 gw s 0 eng d
020 $a 9783319264622$q(electronic bk.)
020 $a 9783319264608$q(paper)
024 7 $a 10.1007/978-3-319-26462-2 $2 doi
035 $a 978-3-319-26462-2
040 $a GP $c GP
041 0 $a eng
050 4 $a TA347.E96
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.3823 $2 23
090 $a TA347.E96 $b C965 2016
100 1 $a Cuevas, Erik. $3 2180030
245 1 0 $a Applications of evolutionary computation in image processing and pattern recognition $h [electronic resource] / $c by Erik Cuevas, Daniel Zaldivar, Marco Perez-Cisneros.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xv, 274 p. : $b ill., digital ; $c 24 cm.
490 1 $a Intelligent systems reference library, $x 1868-4394 ; $v v.100
505 0 $a Introduction -- Image Segmentation Based on Differential Evolution Optimization -- Motion Estimation Based on Artificial Bee Colony (ABC) -- Ellipse Detection on Images Inspired by the Collective Animal Behavior -- Template Matching by Using the States of Matter Algorithm -- Estimation of Multiple View Relations Considering Evolutionary Approaches -- Circle Detection on Images Based on an Evolutionary Algorithm that Reduces the Number of Function Evaluations -- Otsu and Kapur Segmentation Based on Harmony Search Optimization -- Leukocyte Detection by Using Electromagnetism-Like Optimization -- Automatic Segmentation by Using an Algorithm Based on the Behavior of Locust Swarms.
520 $a This book presents the use of efficient Evolutionary Computation (EC) algorithms for solving diverse real-world image processing and pattern recognition problems. It provides an overview of the different aspects of evolutionary methods in order to enable the reader in reaching a global understanding of the field and, in conducting studies on specific evolutionary techniques that are related to applications in image processing and pattern recognition. It explains the basic ideas of the proposed applications in a way that can also be understood by readers outside of the field. Image processing and pattern recognition practitioners who are not evolutionary computation researchers will appreciate the discussed techniques beyond simple theoretical tools since they have been adapted to solve significant problems that commonly arise on such areas. On the other hand, members of the evolutionary computation community can learn the way in which image processing and pattern recognition problems can be translated into an optimization task. The book has been structured so that each chapter can be read independently from the others. It can serve as reference book for students and researchers with basic knowledge in image processing and EC methods.
650 0 $a Evolutionary computation. $3 582189
650 0 $a Image processing $x Mathematics. $3 579707
650 0 $a Image processing. $3 621117
650 0 $a Pattern recognition systems. $3 527885
650 1 4 $a Engineering. $3 586835
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
650 2 4 $a Signal, Image and Speech Processing. $3 891073
650 2 4 $a Image Processing and Computer Vision. $3 891070
650 2 4 $a Calculus of Variations and Optimal Control; Optimization. $3 898674
700 1 $a Zaldivar, Daniel. $3 2180031
700 1 $a Perez-Cisneros, Marco. $3 2180032
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Intelligent systems reference library ; $v v.24. $3 1566491
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-26462-2
950 $a Engineering (Springer-11647)