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Advances in Soft Computing and Machine Learning in Image Processing [electronic resource] / edited by Aboul Ella Hassanien, Diego Alberto Oliva.

Contributor(s): Hassanien, Aboul Ella [editor.] | Oliva, Diego Alberto [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Computational Intelligence: 730Publisher: Cham : Springer International Publishing : Imprint: Springer, 2018Edition: 1st ed. 2018.Description: XII, 718 p. 309 illus., 195 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319637549.Subject(s): Computational intelligence | Artificial intelligence | Signal processing | Computational Intelligence | Artificial Intelligence | Signal, Speech and Image ProcessingAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Color Spaces Advantages and Disadvantages in Image Color Clustering Segmentation -- Multi-objective Whale Optimization Algorithm for Multi-level Thresholding Segmentation -- Evaluating Swarm Optimization Algorithms for Segmentation of Liver Images -- Thermal Image Segmentation Using Evolutionary Computation Techniques -- News Videos Segmentation Using Dominant Colors Representation.
In: Springer Nature eBookSummary: This book is a collection of the latest applications of methods from soft computing and machine learning in image processing. It explores different areas ranging from image segmentation to the object recognition using complex approaches, and includes the theory of the methodologies used to provide an overview of the application of these tools in image processing. The material has been compiled from a scientific perspective, and the book is primarily intended for undergraduate and postgraduate science, engineering, and computational mathematics students. It can also be used for courses on artificial intelligence, advanced image processing, and computational intelligence, and is a valuable resource for researchers in the evolutionary computation, artificial intelligence and image processing communities.
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Color Spaces Advantages and Disadvantages in Image Color Clustering Segmentation -- Multi-objective Whale Optimization Algorithm for Multi-level Thresholding Segmentation -- Evaluating Swarm Optimization Algorithms for Segmentation of Liver Images -- Thermal Image Segmentation Using Evolutionary Computation Techniques -- News Videos Segmentation Using Dominant Colors Representation.

This book is a collection of the latest applications of methods from soft computing and machine learning in image processing. It explores different areas ranging from image segmentation to the object recognition using complex approaches, and includes the theory of the methodologies used to provide an overview of the application of these tools in image processing. The material has been compiled from a scientific perspective, and the book is primarily intended for undergraduate and postgraduate science, engineering, and computational mathematics students. It can also be used for courses on artificial intelligence, advanced image processing, and computational intelligence, and is a valuable resource for researchers in the evolutionary computation, artificial intelligence and image processing communities.

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