大葉大學圖書館 |

Hybrid soft computing for image segmentation[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	006.6
書名/作者:	Hybrid soft computing for image segmentation/ edited by Siddhartha Bhattacharyya ... [et al.].
其他作者:	Bhattacharyya, Siddhartha.
出版者:	Cham : : Springer International Publishing :, 2016.
面頁冊數:	xvi, 321 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Image segmentation.
標題:	Soft computing.
標題:	Computer Science.
標題:	Artificial Intelligence (incl. Robotics)
標題:	Computational Intelligence.
標題:	Computer Imaging, Vision, Pattern Recognition and Graphics.
ISBN:	9783319472232
ISBN:	9783319472225
內容註:	Hybrid Soft Computing Techniques for Image Segmentation: Fundamentals and Applications -- Enhanced Rough-Fuzzy C-Means Algorithm for Image Segmentation -- Intuitionistic Fuzzy C-means Clustering Algorithm for Brain Image Segmentation -- Automatic Segmentation Approaches -- Modified Level Set Segmentation -- Fuzzy Deformable Models for 3D Segmentation of Brain Structures -- Rough Sets for Probabilistic Model Based Image Segmentation -- Segmentation of Cerebral Images.
摘要、提要註:	This book proposes soft computing techniques for segmenting real-life images in applications such as image processing, image mining, video surveillance, and intelligent transportation systems. The book suggests hybrids deriving from three main approaches: fuzzy systems, primarily used for handling real-life problems that involve uncertainty; artificial neural networks, usually applied for machine cognition, learning, and recognition; and evolutionary computation, mainly used for search, exploration, efficient exploitation of contextual information, and optimization. The contributed chapters discuss both the strengths and the weaknesses of the approaches, and the book will be valuable for researchers and graduate students in the domains of image processing and computational intelligence.
電子資源:	http://dx.doi.org/10.1007/978-3-319-47223-2

Hybrid soft computing for image segmentation[electronic resource] /
Hybrid soft computing for image segmentation[electronic resource] /edited by Siddhartha Bhattacharyya ... [et al.]. - Cham :Springer International Publishing :2016. - xvi, 321 p. :ill., digital ;24 cm.

Hybrid Soft Computing Techniques for Image Segmentation: Fundamentals and Applications -- Enhanced Rough-Fuzzy C-Means Algorithm for Image Segmentation -- Intuitionistic Fuzzy C-means Clustering Algorithm for Brain Image Segmentation -- Automatic Segmentation Approaches -- Modified Level Set Segmentation -- Fuzzy Deformable Models for 3D Segmentation of Brain Structures -- Rough Sets for Probabilistic Model Based Image Segmentation -- Segmentation of Cerebral Images.

This book proposes soft computing techniques for segmenting real-life images in applications such as image processing, image mining, video surveillance, and intelligent transportation systems. The book suggests hybrids deriving from three main approaches: fuzzy systems, primarily used for handling real-life problems that involve uncertainty; artificial neural networks, usually applied for machine cognition, learning, and recognition; and evolutionary computation, mainly used for search, exploration, efficient exploitation of contextual information, and optimization. The contributed chapters discuss both the strengths and the weaknesses of the approaches, and the book will be valuable for researchers and graduate students in the domains of image processing and computational intelligence.

ISBN: 9783319472232

Standard No.: 10.1007/978-3-319-47223-2doiSubjects--Topical Terms:

602455
Image segmentation.

LC Class. No.: TA1632 / .H93 2016

Dewey Class. No.: 006.6

Hybrid soft computing for image segmentation[electronic resource] /
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505 0 $a Hybrid Soft Computing Techniques for Image Segmentation: Fundamentals and Applications -- Enhanced Rough-Fuzzy C-Means Algorithm for Image Segmentation -- Intuitionistic Fuzzy C-means Clustering Algorithm for Brain Image Segmentation -- Automatic Segmentation Approaches -- Modified Level Set Segmentation -- Fuzzy Deformable Models for 3D Segmentation of Brain Structures -- Rough Sets for Probabilistic Model Based Image Segmentation -- Segmentation of Cerebral Images.
520 $a This book proposes soft computing techniques for segmenting real-life images in applications such as image processing, image mining, video surveillance, and intelligent transportation systems. The book suggests hybrids deriving from three main approaches: fuzzy systems, primarily used for handling real-life problems that involve uncertainty; artificial neural networks, usually applied for machine cognition, learning, and recognition; and evolutionary computation, mainly used for search, exploration, efficient exploitation of contextual information, and optimization. The contributed chapters discuss both the strengths and the weaknesses of the approaches, and the book will be valuable for researchers and graduate students in the domains of image processing and computational intelligence.
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