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Hybrid Soft Computing for Image Segmentation

Editat de Siddhartha Bhattacharyya, Paramartha Dutta, Sourav De, Goran Klepac
en Limba Engleză Paperback – 29 iun 2018
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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Specificații

ISBN-13: 9783319836843
ISBN-10: 3319836846
Pagini: 321
Ilustrații: XVI, 321 p. 162 illus., 87 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.48 kg
Ediția:Softcover reprint of the original 1st ed. 2016
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland

Cuprins

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. 

Textul de pe ultima copertă

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.

Caracteristici

Topic important in applications such as image processing, image mining, video surveillance, and intelligent transportation systems Valuable for researchers and graduate students in the domains of image processing and computational intelligence Contributions examine strengths and weaknesses of the approaches Includes supplementary material: sn.pub/extras