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Hybrid Soft Computing for Multilevel Image and Data Segmentation de Sourav De

Hybrid Soft Computing for Multilevel Image and Data Segmentation: Computational Intelligence Methods and Applications

Autor Sourav De, Siddhartha Bhattacharyya, Susanta Chakraborty, Paramartha Dutta
en Limba Engleză Paperback – 29 iun 2018
This book explains efficient solutions for segmenting the intensity levels of different types of multilevel images. The authors present hybrid soft computing techniques, which have advantages over conventional soft computing solutions as they incorporate data heterogeneity into the clustering/segmentation procedures.

This is a useful introduction and reference for researchers and graduate students of computer science and electronics engineering, particularly in the domains of image processing and computational intelligence.
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Specificații

ISBN-13: 9783319837581
ISBN-10: 3319837583
Ilustrații: XIV, 235 p. 99 illus., 39 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.36 kg
Ediția:Softcover reprint of the original 1st ed. 2016
Editura: Springer International Publishing
Colecția Springer
Seria Computational Intelligence Methods and Applications

Locul publicării:Cham, Switzerland

Cuprins

Introduction.- Image Segmentation: A Review.- Self-supervised Gray Level Image Segmentation Using an Optimized MUSIG (OptiMUSIG) Activation Function.- Self-supervised Color Image Segmentation Using Parallel OptiMUSIG (ParaOptiMUSIG) Activation Function.- Self-supervised Gray Level Image Segmentation Using Multiobjective Based Optimized MUSIG (OptiMUSIG) Activation Function.- Self-supervised Color Image Segmentation Using Multiobjective Based Parallel Optimized MUSIG (ParaOptiMUSIG) Activation Function.- Unsupervised Genetic Algorithm Based Automatic Image Segmentation and Data Clustering Technique Validated by Fuzzy Intercluster Hostility Index.

Textul de pe ultima copertă

This book explains efficient solutions for segmenting the intensity levels of different types of multilevel images. The authors present hybrid soft computing techniques, which have advantages over conventional soft computing solutions as they incorporate data heterogeneity into the clustering/segmentation procedures.
This is a useful introduction and reference for researchers and graduate students of computer science and electronics engineering, particularly in the domains of image processing and computational intelligence.

Caracteristici

Explains important image processing approach Contributes to broader objective, the development of robust efficient fail-safe hybrid intelligent systems Valuable for researchers and graduate students in the domains of image processing and computational intelligence Includes supplementary material: sn.pub/extras