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Applications of Hybrid Metaheuristic Algorithms for Image Processing

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Applications of Hybrid Metaheuristic Algorithms for Image Processing/ edited by Diego Oliva, Salvador Hinojosa.
other author:	Oliva, Diego.
Description:	IX, 490 p. 311 illus., 221 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computational intelligence. -
Online resource:	https://doi.org/10.1007/978-3-030-40977-7
ISBN:	9783030409777

Applications of Hybrid Metaheuristic Algorithms for Image Processing
Applications of Hybrid Metaheuristic Algorithms for Image Processing[electronic resource] /edited by Diego Oliva, Salvador Hinojosa. - 1st ed. 2020. - IX, 490 p. 311 illus., 221 illus. in color.online resource. - Studies in Computational Intelligence,8901860-949X ;. - Studies in Computational Intelligence,564.

Segmentation Of Thermal Images By Means Of Metaheuristic Algorithms, For Failure Detection And Preventive Maintenance Of Electronic Systems -- A Survey on Image Processing for Hyperspectral and Remote Sensing Images -- Hybrid Grey-Wolf Optimizer Based Fractional Order Optimal Filtering for Texture Aware Quality Enhancement for Remotely Sensed Images -- Robust K- Means Technique For Band Reduction On Hyperspectral Image Segmentation -- Ethnic Characterization In Amalgamated People For Airport Security Using A Repository Of Images And Pigeon-Inspired Optimization (Pio) Algorithm For The Improvement Of Their Results} -- Multi-Level Image Thresholding Segmentation Using Non-Local Means 2d Histogram And Metaheuristics Algorithms -- Comparison Of Metaheuristic Methods For Template Matching -- Novel Feature Extraction Strategies Supporting 2d Shape Description And Retrieval -- Clustering Data Using Techniques Of Image Processing Erode And Dilate To Avoid The Use Of Euclidian Distance -- Estimation Of The Homography Matrix To Image Stitching -- Active Contour Model in Deep Learning Era: A Revise and Review -- Comparison of Evolutionary Techniques for Car Accident Prediction -- Salp Swarm Algorithm: A Comprehensive Review -- Segmentation Of Magnetic Resonance Brain Images Through The Self-Adaptive Differential Evolution Algorithm And The Minimum Cross-Entropy Criterion -- Automatic Detection Of Malignant Masses In Digital Mammograms Based On A Mcet-Hho Approach -- Cancer Cell Prediction using Machine Learning and Evolutionary Algorithms -- Meta Heuristic Approach of RMDL Classification of Parkinson's disease -- Fuzzy- Crow Search Optimization for Medical Image Segmentation -- Intelligent System For The Visual Support Of Caloric Intake Of Food In Inhabitants Of A Smart City Using A Deep Learning Model -- Image Thresholding with Metaheuristic Algorithms for Cerebral Injuries -- Generative Adversarial Network And Retinal Image Segmentation.

This book presents a collection of the most recent hybrid methods for image processing. The algorithms included consider evolutionary, swarm, machine learning and deep learning. The respective chapters explore different areas of image processing, from image segmentation to the recognition of objects using complex approaches and medical applications. The book also discusses the theory of the methodologies used to provide an overview of the applications of these tools in image processing. The book is primarily intended for undergraduate and postgraduate students of science, engineering and computational mathematics, and can also be used for courses on artificial intelligence, advanced image processing, and computational intelligence. Further, it is a valuable resource for researchers from the evolutionary computation, artificial intelligence and image processing communities.

ISBN: 9783030409777

Standard No.: 10.1007/978-3-030-40977-7doiSubjects--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Applications of Hybrid Metaheuristic Algorithms for Image Processing
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520 $a This book presents a collection of the most recent hybrid methods for image processing. The algorithms included consider evolutionary, swarm, machine learning and deep learning. The respective chapters explore different areas of image processing, from image segmentation to the recognition of objects using complex approaches and medical applications. The book also discusses the theory of the methodologies used to provide an overview of the applications of these tools in image processing. The book is primarily intended for undergraduate and postgraduate students of science, engineering and computational mathematics, and can also be used for courses on artificial intelligence, advanced image processing, and computational intelligence. Further, it is a valuable resource for researchers from the evolutionary computation, artificial intelligence and image processing communities.
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