Hybrid Soft Computing for Multilevel Image and Data Segmentation

• Κύριοι συγγραφείς: De, Sourav (Συγγραφέας), Bhattacharyya, Siddhartha (Συγγραφέας), Chakraborty, Susanta (Συγγραφέας), Dutta, Paramartha (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2016.
• Σειρά: Computational Intelligence Methods and Applications,
• Θέματα: Computer science.; Artificial intelligence.; Computer graphics.; Computational intelligence.; Computer Science.; Artificial Intelligence (incl. Robotics).; Computational Intelligence.; Computer Imaging, Vision, Pattern Recognition and Graphics.

 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. 

 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.