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Hybrid metaheuristics for image analysis
~
Bhattacharyya, Siddhartha.
Hybrid metaheuristics for image analysis
Record Type:
Electronic resources : Monograph/item
Title/Author:
Hybrid metaheuristics for image analysisedited by Siddhartha Bhattacharyya.
other author:
Bhattacharyya, Siddhartha.
Published:
Cham :Springer International Publishing :2018.
Description:
xii, 256 p. :ill., digital ;24 cm.
Contained By:
Springer eBooks
Subject:
Image analysis.
Online resource:
http://dx.doi.org/10.1007/978-3-319-77625-5
ISBN:
9783319776255$q(electronic bk.)
Hybrid metaheuristics for image analysis
Hybrid metaheuristics for image analysis
[electronic resource] /edited by Siddhartha Bhattacharyya. - Cham :Springer International Publishing :2018. - xii, 256 p. :ill., digital ;24 cm.
Current and Future Trends in Segmenting Satellite Images Using Hybrid and Dynamic Genetic Algorithms -- A Hybrid Metaheuristic Algorithm Based on Quantum Genetic Computing for Image Segmentation -- Genetic Algorithm Implementation to Optimize the Hybridization of Feature Extraction and Metaheuristic Classifiers -- Optimization of a HMM-Based Hand Gesture Recognition System Using a Hybrid Cuckoo Search Algorithm -- Satellite Image Contrast Enhancement Using Fuzzy Termite Colony Optimization -- Image Segmentation Using Metaheuristic-Based DeformableModels -- Hybridization of the Univariate Marginal Distribution Algorithm with Simulated Annealing for Parametric Parabola Detection -- Image Thresholding Based on Fuzzy Particle Swarm Optimization -- Hybrid Metaheuristics Applied to Image Reconstruction for an Electrical Impedance Tomography Prototype.
This book presents contributions in the field of computational intelligence for the purpose of image analysis. The chapters discuss how problems such as image segmentation, edge detection, face recognition, feature extraction, and image contrast enhancement can be solved using techniques such as genetic algorithms and particle swarm optimization. The contributions provide a multidimensional approach, and the book will be useful for researchers in computer science, electrical engineering, and information technology.
ISBN: 9783319776255$q(electronic bk.)
Standard No.: 10.1007/978-3-319-77625-5doiSubjects--Topical Terms:
190419
Image analysis.
LC Class. No.: TA1637 / .H93 2018
Dewey Class. No.: 621.367
Hybrid metaheuristics for image analysis
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Current and Future Trends in Segmenting Satellite Images Using Hybrid and Dynamic Genetic Algorithms -- A Hybrid Metaheuristic Algorithm Based on Quantum Genetic Computing for Image Segmentation -- Genetic Algorithm Implementation to Optimize the Hybridization of Feature Extraction and Metaheuristic Classifiers -- Optimization of a HMM-Based Hand Gesture Recognition System Using a Hybrid Cuckoo Search Algorithm -- Satellite Image Contrast Enhancement Using Fuzzy Termite Colony Optimization -- Image Segmentation Using Metaheuristic-Based DeformableModels -- Hybridization of the Univariate Marginal Distribution Algorithm with Simulated Annealing for Parametric Parabola Detection -- Image Thresholding Based on Fuzzy Particle Swarm Optimization -- Hybrid Metaheuristics Applied to Image Reconstruction for an Electrical Impedance Tomography Prototype.
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This book presents contributions in the field of computational intelligence for the purpose of image analysis. The chapters discuss how problems such as image segmentation, edge detection, face recognition, feature extraction, and image contrast enhancement can be solved using techniques such as genetic algorithms and particle swarm optimization. The contributions provide a multidimensional approach, and the book will be useful for researchers in computer science, electrical engineering, and information technology.
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Image analysis.
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Computer Science (Springer-11645)
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EB TA1637 H992 2018 2018
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http://dx.doi.org/10.1007/978-3-319-77625-5
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