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Hybrid Soft Computing for Multilevel Image and Data Segmentation [electronic resource] / by Sourav De, Siddhartha Bhattacharyya, Susanta Chakraborty, Paramartha Dutta.

By: De, Sourav [author.].
Contributor(s): Bhattacharyya, Siddhartha [author.] | Chakraborty, Susanta [author.] | Dutta, Paramartha [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Computational Intelligence Methods and Applications: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2016Description: XIV, 235 p. 99 illus., 39 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319475240.Subject(s): Computer science | Artificial intelligence | Computer graphics | Computational intelligence | Computer Science | Artificial Intelligence (incl. Robotics) | Computational Intelligence | Computer Imaging, Vision, Pattern Recognition and GraphicsAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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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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.

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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