Hybrid Metaheuristics For Image Analysis
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Hybrid Metaheuristics For Image Analysis
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Author : Siddhartha Bhattacharyya
language : en
Publisher: Springer
Release Date : 2018-07-30
Hybrid Metaheuristics For Image Analysis written by Siddhartha Bhattacharyya and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-30 with Computers categories.
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.
Applications Of Hybrid Metaheuristic Algorithms For Image Processing
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Author : Diego Oliva
language : en
Publisher: Springer Nature
Release Date : 2020-03-27
Applications Of Hybrid Metaheuristic Algorithms For Image Processing written by Diego Oliva and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-27 with Technology & Engineering categories.
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.
Metaheuristic Algorithms For Image Segmentation Theory And Applications
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Author : Diego Oliva
language : en
Publisher: Springer
Release Date : 2019-03-02
Metaheuristic Algorithms For Image Segmentation Theory And Applications written by Diego Oliva and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-02 with Technology & Engineering categories.
This book presents a study of the most important methods of image segmentation and how they are extended and improved using metaheuristic algorithms. The segmentation approaches selected have been extensively applied to the task of segmentation (especially in thresholding), and have also been implemented using various metaheuristics and hybridization techniques leading to a broader understanding of how image segmentation problems can be solved from an optimization perspective. The field of image processing is constantly changing due to the extensive integration of cameras in devices; for example, smart phones and cars now have embedded cameras. The images have to be accurately analyzed, and crucial pre-processing steps, like image segmentation, and artificial intelligence, including metaheuristics, are applied in the automatic analysis of digital images. Metaheuristic algorithms have also been used in various fields of science and technology as the demand for new methods designed to solve complex optimization problems increases. This didactic book is primarily intended for undergraduate and postgraduate students of science, engineering, and computational mathematics. It is also suitable for courses such as artificial intelligence, advanced image processing, and computational intelligence. The material is also useful for researches in the fields of evolutionary computation, artificial intelligence, and image processing.
Image Segmentation Through Metaheuristics Optimization
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Author : Thuy Xuan Pham
language : en
Publisher:
Release Date : 2019
Image Segmentation Through Metaheuristics Optimization written by Thuy Xuan Pham and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Image segmentation is the process of partitioning an image into smaller non-overlapped and meaningful regions based in part on some homogeneity characteristics. Many high-level processing tasks such as feature extraction, object recognition and medical diagnosis depend heavily on the quality of solutions. In medical image analysis, images usually contain some artifacts such as noise, image volume effect and bias field effect due to various factors, for instance, environment and acquisition devices, and have complex structures. Therefore, image segmentation remains a difficult task even if various techniques and methods of different accuracy and degree of complexity have been introduced in the literature. Several approaches such as fuzzy clustering, region-based active contour, Markov random field, have been found that can produce promising results; however, still many key open issues remain to be investigated. Up to now, there is no gold standard method and segmentation procedures still need a significant amount of expert intervention for improving the performance.Metaheuristics are a high-level procedure designed to solve optimization problems by the process of searching optimal solutions to a particular problem of interest. Metaheuristics are generally applied to problems for which there is no satisfactory algorithm able to solve them effectively. Therefore, they are widely used to solve complex problems and have proven to be successful in many fields of application with varying degrees of success. Considering the image segmentation problem as one of the optimization problems solved by metaheuristics, image segmentation has attracted many researchers in recent years. In many successful applications, it can be seen that the traditional approaches for image segmentation have been combined with metaheuristics in different perspectives in order to improve their performance.Bearing those in mind, we propose in this work three image segmentation methods for magnetic resonance (MR) brain images based on mono-objective, multi-objective and hybrid metaheuristic optimization techniques. In each method, first, the basic model for the image segmentation problem is extended to incorporate more image information (spatial or spectral) such that more and better characteristics in segmented image can be achieved. Then, metaheuristic algorithms are adapted or developed to take place in optimization step. The proposed methods were evaluated on both simulated MR images and real MR images and compared with a set of recent methods in the literature. The obtained results show clearly the efficiency of the proposed ideas.Keywords: Image segmentation, fuzzy clustering, region-based active contour, Markov random field, metaheuristics, multi-objective optimization, hybrid metaheuristic, MRI.
Recent Advances In Hybrid Metaheuristics For Data Clustering
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Author : Sourav De
language : en
Publisher: John Wiley & Sons
Release Date : 2020-06-02
Recent Advances In Hybrid Metaheuristics For Data Clustering written by Sourav De and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-02 with Computers categories.
An authoritative guide to an in-depth analysis of various state-of-the-art data clustering approaches using a range of computational intelligence techniques Recent Advances in Hybrid Metaheuristics for Data Clustering offers a guide to the fundamentals of various metaheuristics and their application to data clustering. Metaheuristics are designed to tackle complex clustering problems where classical clustering algorithms have failed to be either effective or efficient. The authors noted experts on the topic provide a text that can aid in the design and development of hybrid metaheuristics to be applied to data clustering. The book includes performance analysis of the hybrid metaheuristics in relationship to their conventional counterparts. In addition to providing a review of data clustering, the authors include in-depth analysis of different optimization algorithms. The text offers a step-by-step guide in the build-up of hybrid metaheuristics and to enhance comprehension. In addition, the book contains a range of real-life case studies and their applications. This important text: Includes performance analysis of the hybrid metaheuristics as related to their conventional counterparts Offers an in-depth analysis of a range of optimization algorithms Highlights a review of data clustering Contains a detailed overview of different standard metaheuristics in current use Presents a step-by-step guide to the build-up of hybrid metaheuristics Offers real-life case studies and applications Written for researchers, students and academics in computer science, mathematics, and engineering, Recent Advances in Hybrid Metaheuristics for Data Clustering provides a text that explores the current data clustering approaches using a range of computational intelligence techniques.
Hybrid Quantum Metaheuristics
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Author : Siddhartha Bhattacharyya
language : en
Publisher: CRC Press
Release Date : 2022-05-07
Hybrid Quantum Metaheuristics written by Siddhartha Bhattacharyya and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-07 with Technology & Engineering categories.
The reference text introduces the principles of quantum mechanics to evolve hybrid metaheuristics-based optimization techniques useful for real world engineering and scientific problems. The text covers advances and trends in methodological approaches, theoretical studies, mathematical and applied techniques related to hybrid quantum metaheuristics and their applications to engineering problems. The book will be accompanied by additional resources including video demonstration for each chapter. It will be a useful text for graduate students and professional in the field of electrical engineering, electronics and communications engineering, and computer science engineering, this text: Discusses quantum mechanical principles in detail. Emphasizes the recent and upcoming hybrid quantum metaheuristics in a comprehensive manner. Provides comparative statistical test analysis with conventional hybrid metaheuristics. Highlights real-life case studies, applications, and video demonstrations.
Quantum Inspired Meta Heuristics For Image Analysis
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Author : Sandip Dey
language : en
Publisher: John Wiley & Sons
Release Date : 2019-05-29
Quantum Inspired Meta Heuristics For Image Analysis written by Sandip Dey and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-29 with Technology & Engineering categories.
Introduces quantum inspired techniques for image analysis for pure and true gray scale/color images in a single/multi-objective environment This book will entice readers to design efficient meta-heuristics for image analysis in the quantum domain. It introduces them to the essence of quantum computing paradigm, its features, and properties, and elaborates on the fundamentals of different meta-heuristics and their application to image analysis. As a result, it will pave the way for designing and developing quantum computing inspired meta-heuristics to be applied to image analysis. Quantum Inspired Meta-heuristics for Image Analysis begins with a brief summary on image segmentation, quantum computing, and optimization. It also highlights a few relevant applications of the quantum based computing algorithms, meta-heuristics approach, and several thresholding algorithms in vogue. Next, it discusses a review of image analysis before moving on to an overview of six popular meta-heuristics and their algorithms and pseudo-codes. Subsequent chapters look at quantum inspired meta-heuristics for bi-level and gray scale multi-level image thresholding; quantum behaved meta-heuristics for true color multi-level image thresholding; and quantum inspired multi-objective algorithms for gray scale multi-level image thresholding. Each chapter concludes with a summary and sample questions. Provides in-depth analysis of quantum mechanical principles Offers comprehensive review of image analysis Analyzes different state-of-the-art image thresholding approaches Detailed current, popular standard meta-heuristics in use today Guides readers step by step in the build-up of quantum inspired meta-heuristics Includes a plethora of real life case studies and applications Features statistical test analysis of the performances of the quantum inspired meta-heuristics vis-à-vis their conventional counterparts Quantum Inspired Meta-heuristics for Image Analysis is an excellent source of information for anyone working with or learning quantum inspired meta-heuristics for image analysis.
Quantum Inspired Meta Heuristics For Image Analysis
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Author : Sandip Dey
language : en
Publisher: John Wiley & Sons
Release Date : 2019-08-05
Quantum Inspired Meta Heuristics For Image Analysis written by Sandip Dey and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-05 with Technology & Engineering categories.
Introduces quantum inspired techniques for image analysis for pure and true gray scale/color images in a single/multi-objective environment This book will entice readers to design efficient meta-heuristics for image analysis in the quantum domain. It introduces them to the essence of quantum computing paradigm, its features, and properties, and elaborates on the fundamentals of different meta-heuristics and their application to image analysis. As a result, it will pave the way for designing and developing quantum computing inspired meta-heuristics to be applied to image analysis. Quantum Inspired Meta-heuristics for Image Analysis begins with a brief summary on image segmentation, quantum computing, and optimization. It also highlights a few relevant applications of the quantum based computing algorithms, meta-heuristics approach, and several thresholding algorithms in vogue. Next, it discusses a review of image analysis before moving on to an overview of six popular meta-heuristics and their algorithms and pseudo-codes. Subsequent chapters look at quantum inspired meta-heuristics for bi-level and gray scale multi-level image thresholding; quantum behaved meta-heuristics for true color multi-level image thresholding; and quantum inspired multi-objective algorithms for gray scale multi-level image thresholding. Each chapter concludes with a summary and sample questions. Provides in-depth analysis of quantum mechanical principles Offers comprehensive review of image analysis Analyzes different state-of-the-art image thresholding approaches Detailed current, popular standard meta-heuristics in use today Guides readers step by step in the build-up of quantum inspired meta-heuristics Includes a plethora of real life case studies and applications Features statistical test analysis of the performances of the quantum inspired meta-heuristics vis-à-vis their conventional counterparts Quantum Inspired Meta-heuristics for Image Analysis is an excellent source of information for anyone working with or learning quantum inspired meta-heuristics for image analysis.
Metaheuristics And Optimization In Computer And Electrical Engineering
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Author : Navid Razmjooy
language : en
Publisher: Springer Nature
Release Date : 2023-11-08
Metaheuristics And Optimization In Computer And Electrical Engineering written by Navid Razmjooy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-08 with Technology & Engineering categories.
This book discusses different methods of modifying the original metaheuristics and their application in computer and electrical engineering. As the race to develop advanced technology accelerates, a new era of "metaheuristics" has emerged. Through researched-based techniques and collaborative problem-solving, this book helps engineers to find efficient solutions to their engineering challenges. With the help of an expert guide and the collective knowledge of the engineering community, this comprehensive guide shows readers how to use machine learning and other AI techniques to reinvent smart engineering. From understanding the fundamentals to mastering the latest metaheuristics models, this guide provides with the skills and knowledge that need to stay ahead in the technology race. In the previous volume, authors focused on the application of original metaheuristics on electrical and computer sciences. This volume learns how AI and modified metaheuristics can be used to optimize algorithms and create more efficient electrical engineering designs. It gets insights on how data can be effectively processed and discover new techniques for creating sophisticated automation systems. It maximizes the potential of readers’ computer and electrical engineering projects with powerful metaheuristics and optimization techniques.
Recent Advances In Hybrid Metaheuristics For Data Clustering
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Author : Sourav De
language : en
Publisher: John Wiley & Sons
Release Date : 2020-06-02
Recent Advances In Hybrid Metaheuristics For Data Clustering written by Sourav De and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-02 with Computers categories.
An authoritative guide to an in-depth analysis of various state-of-the-art data clustering approaches using a range of computational intelligence techniques Recent Advances in Hybrid Metaheuristics for Data Clustering offers a guide to the fundamentals of various metaheuristics and their application to data clustering. Metaheuristics are designed to tackle complex clustering problems where classical clustering algorithms have failed to be either effective or efficient. The authors—noted experts on the topic—provide a text that can aid in the design and development of hybrid metaheuristics to be applied to data clustering. The book includes performance analysis of the hybrid metaheuristics in relationship to their conventional counterparts. In addition to providing a review of data clustering, the authors include in-depth analysis of different optimization algorithms. The text offers a step-by-step guide in the build-up of hybrid metaheuristics and to enhance comprehension. In addition, the book contains a range of real-life case studies and their applications. This important text: Includes performance analysis of the hybrid metaheuristics as related to their conventional counterparts Offers an in-depth analysis of a range of optimization algorithms Highlights a review of data clustering Contains a detailed overview of different standard metaheuristics in current use Presents a step-by-step guide to the build-up of hybrid metaheuristics Offers real-life case studies and applications Written for researchers, students and academics in computer science, mathematics, and engineering, Recent Advances in Hybrid Metaheuristics for Data Clustering provides a text that explores the current data clustering approaches using a range of computational intelligence techniques.