東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"Image segmentation." ]

切換: 標籤 | MARC模式 | ISBD

Metaheuristic algorithms for image s...

Oliva, Diego.

FindBook

Google Book

Amazon

博客來

Metaheuristic algorithms for image segmentation = theory and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Metaheuristic algorithms for image segmentation/ by Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa.
其他題名:	theory and applications /
作者:	Oliva, Diego.
其他作者:	Abd Elaziz, Mohamed.
出版者:	Cham :Springer International Publishing : : 2019.,
面頁冊數:	xv, 226 p. :ill. (some col.), digital ;24 cm.
內容註:	Introduction -- Optimization -- Metaheuristic optimization -- Image processing -- Image Segmentation using metaheuristics -- Multilevel thresholding for image segmentation based on metaheuristic Algorithms -- Otsu's between class variance and the tree seed algorithm -- Image segmentation using Kapur's entropy and a hybrid optimization algorithm -- Tsallis entropy for image thresholding -- Image segmentation with minimum cross entropy -- Fuzzy entropy approaches for image segmentation -- Image segmentation by gaussian mixture -- Image segmentation as a multiobjective optimization problem -- Clustering algorithms for image segmentation -- Contextual information in image thresholding.
Contained By:	Springer eBooks
標題:	Image segmentation. -
電子資源:	https://doi.org/10.1007/978-3-030-12931-6
ISBN:	9783030129316

Metaheuristic algorithms for image segmentation = theory and applications /
Oliva, Diego.

Metaheuristic algorithms for image segmentationtheory and applications /[electronic resource] :by Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa. - Cham :Springer International Publishing :2019. - xv, 226 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.8251860-949X ;. - Studies in computational intelligence ;v.825..

Introduction -- Optimization -- Metaheuristic optimization -- Image processing -- Image Segmentation using metaheuristics -- Multilevel thresholding for image segmentation based on metaheuristic Algorithms -- Otsu's between class variance and the tree seed algorithm -- Image segmentation using Kapur's entropy and a hybrid optimization algorithm -- Tsallis entropy for image thresholding -- Image segmentation with minimum cross entropy -- Fuzzy entropy approaches for image segmentation -- Image segmentation by gaussian mixture -- Image segmentation as a multiobjective optimization problem -- Clustering algorithms for image segmentation -- Contextual information in image thresholding.

This book presents a study of the most important methods of image segmentation and how they are extended and improved using metaheuristic algorithms. The segmentation approaches selected have been extensively applied to the task of segmentation (especially in thresholding), and have also been implemented using various metaheuristics and hybridization techniques leading to a broader understanding of how image segmentation problems can be solved from an optimization perspective. The field of image processing is constantly changing due to the extensive integration of cameras in devices; for example, smart phones and cars now have embedded cameras. The images have to be accurately analyzed, and crucial pre-processing steps, like image segmentation, and artificial intelligence, including metaheuristics, are applied in the automatic analysis of digital images. Metaheuristic algorithms have also been used in various fields of science and technology as the demand for new methods designed to solve complex optimization problems increases. This didactic book is primarily intended for undergraduate and postgraduate students of science, engineering, and computational mathematics. It is also suitable for courses such as artificial intelligence, advanced image processing, and computational intelligence. The material is also useful for researches in the fields of evolutionary computation, artificial intelligence, and image processing.

ISBN: 9783030129316

Standard No.: 10.1007/978-3-030-12931-6doiSubjects--Topical Terms:

2132796
Image segmentation.

LC Class. No.: TA1638.4 / .O458 2019

Dewey Class. No.: 006.6

Metaheuristic algorithms for image segmentation = theory and applications /
LDR:03251nmm a2200337 a 4500 001 2179950
003 DE-He213
005 20190905140941.0
006 m d
007 cr nn 008maaau
008 191122s2019 gw s 0 eng d
020 $a 9783030129316 $q (hardback)
020 $a 9783030129309 $q (paper)
024 7 $a 10.1007/978-3-030-12931-6 $2 doi
035 $a 978-3-030-12931-6
040 $a GP $c GP
041 0 $a eng
050 4 $a TA1638.4 $b .O458 2019
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.6 $2 23
090 $a TA1638.4 $b .O48 2019
100 1 $a Oliva, Diego. $3 3218447
245 1 0 $a Metaheuristic algorithms for image segmentation $h [electronic resource] : $b theory and applications / $c by Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xv, 226 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v v.825
505 0 $a Introduction -- Optimization -- Metaheuristic optimization -- Image processing -- Image Segmentation using metaheuristics -- Multilevel thresholding for image segmentation based on metaheuristic Algorithms -- Otsu's between class variance and the tree seed algorithm -- Image segmentation using Kapur's entropy and a hybrid optimization algorithm -- Tsallis entropy for image thresholding -- Image segmentation with minimum cross entropy -- Fuzzy entropy approaches for image segmentation -- Image segmentation by gaussian mixture -- Image segmentation as a multiobjective optimization problem -- Clustering algorithms for image segmentation -- Contextual information in image thresholding.
520 $a This book presents a study of the most important methods of image segmentation and how they are extended and improved using metaheuristic algorithms. The segmentation approaches selected have been extensively applied to the task of segmentation (especially in thresholding), and have also been implemented using various metaheuristics and hybridization techniques leading to a broader understanding of how image segmentation problems can be solved from an optimization perspective. The field of image processing is constantly changing due to the extensive integration of cameras in devices; for example, smart phones and cars now have embedded cameras. The images have to be accurately analyzed, and crucial pre-processing steps, like image segmentation, and artificial intelligence, including metaheuristics, are applied in the automatic analysis of digital images. Metaheuristic algorithms have also been used in various fields of science and technology as the demand for new methods designed to solve complex optimization problems increases. This didactic book is primarily intended for undergraduate and postgraduate students of science, engineering, and computational mathematics. It is also suitable for courses such as artificial intelligence, advanced image processing, and computational intelligence. The material is also useful for researches in the fields of evolutionary computation, artificial intelligence, and image processing.
650 0 $a Image segmentation. $3 2132796
650 0 $a Evolutionary computation. $3 582189
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Signal, Image and Speech Processing. $3 891073
700 1 $a Abd Elaziz, Mohamed. $3 3385570
700 1 $a Hinojosa, Salvador. $3 3385571
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Studies in computational intelligence ; $v v.825. $3 3385572
856 4 0 $u https://doi.org/10.1007/978-3-030-12931-6
950 $a Intelligent Technologies and Robotics (Springer-42732)