Download or read book Image Segmentation Through Metaheuristics Optimization written by Thuy Xuan Pham and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Metaheuristic Algorithms for Image Segmentation Theory and Applications written by Diego Oliva and published by Springer. This book was released on 2019-03-02 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Metaheuristics for Data Clustering and Image Segmentation written by Meera Ramadas and published by Springer. This book was released on 2018-12-12 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, differential evolution and its modified variants are applied to the clustering of data and images. Metaheuristics have emerged as potential algorithms for dealing with complex optimization problems, which are otherwise difficult to solve using traditional methods. In this regard, differential evolution is considered to be a highly promising technique for optimization and is being used to solve various real-time problems. The book studies the algorithms in detail, tests them on a range of test images, and carefully analyzes their performance. Accordingly, it offers a valuable reference guide for all researchers, students and practitioners working in the fields of artificial intelligence, optimization and data analytics.

Download or read book Applications of Hybrid Metaheuristic Algorithms for Image Processing written by Diego Oliva and published by Springer Nature. This book was released on 2020-03-27 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Modern Metaheuristics in Image Processing written by Diego Oliva and published by CRC Press. This book was released on 2022-09-28 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of metaheuristic algorithms (MA) has been increasing in recent years, and the image processing field is not the exempted of their application. In the last two years a big amount of MA has been introduced as alternatives for solving complex optimization problems. This book collects the most prominent MA of the 2019 and 2020 and verifies its use in image processing tasks. In addition, literature review of both MA and digital image processing is presented as part of the introductory information. Each algorithm is detailed explained with special focus in the tuning parameters and the proper implementation for the image processing tasks. Besides several examples permits to the reader explore and confirm the use of this kind of intelligent methods. Since image processing is widely used in different domains, this book considers different kinds of datasets that includes, magnetic resonance images, thermal images, agriculture images, among others. The reader then can have some ideas of implementation that complement the theory exposed of each optimization mechanism. Regarding the image processing problems this book consider the segmentation by using different metrics based on entropies or variances. In the same way, the identification of different shapes and the detection of objects are also covered in the corresponding chapters. Each chapter is complemented with a wide range of experiments and statistical analysis that permits the reader to judge about the performance of the MA. Finally, there is included a section that includes some discussion and conclusions. This section also provides some open questions and research opportunities for the audience.

Download or read book Hybrid Metaheuristics for Image Analysis written by Siddhartha Bhattacharyya and published by Springer. This book was released on 2018-07-30 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Quantum Inspired Meta heuristics for Image Analysis written by Sandip Dey and published by John Wiley & Sons. This book was released on 2019-05-29 with total page 6 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Advancements in Applied Metaheuristic Computing written by Dey, Nilanjan and published by IGI Global. This book was released on 2017-11-30 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: Metaheuristic algorithms are present in various applications for different domains. Recently, researchers have conducted studies on the effectiveness of these algorithms in providing optimal solutions to complicated problems. Advancements in Applied Metaheuristic Computing is a crucial reference source for the latest empirical research on methods and approaches that include metaheuristics for further system improvements, and it offers outcomes of employing optimization algorithms. Featuring coverage on a broad range of topics such as manufacturing, genetic programming, and medical imaging, this publication is ideal for researchers, academicians, advanced-level students, and technology developers seeking current research on the use of optimization algorithms in several applications.

Download or read book Integrating Metaheuristics in Computer Vision for Real World Optimization Problems written by Shubham Mahajan and published by John Wiley & Sons. This book was released on 2024-08-27 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive book providing high-quality research addressing challenges in theoretical and application aspects of soft computing and machine learning in image processing and computer vision. Researchers are working to create new algorithms that combine the methods provided by CI approaches to solve the problems of image processing and computer vision such as image size, noise, illumination, and security. The 19 chapters in this book examine computational intelligence (CI) approaches as alternative solutions for automatic computer vision and image processing systems in a wide range of applications, using machine learning and soft computing. Applications highlighted in the book include: diagnostic and therapeutic techniques for ischemic stroke, object detection, tracking face detection and recognition; computational-based strategies for drug repositioning and improving performance with feature selection, extraction, and learning; methods capable of retrieving photometric and geometric transformed images; concepts of trading the cryptocurrency market based on smart price action strategies; comparative evaluation and prediction of exoplanets using machine learning methods; the risk of using failure rate with the help of MTTF and MTBF to calculate reliability; a detailed description of various techniques using edge detection algorithms; machine learning in smart houses; the strengths and limitations of swarm intelligence and computation; how to use bidirectional LSTM for heart arrhythmia detection; a comprehensive study of content-based image-retrieval techniques for feature extraction; machine learning approaches to understanding angiogenesis; handwritten image enhancement based on neutroscopic-fuzzy. Audience The book has been designed for researchers, engineers, graduate, and post-graduate students wanting to learn more about the theoretical and application aspects of soft computing and machine learning in image processing and computer vision.

Download or read book Metaheuristic Optimization Algorithms written by Laith Abualigah and published by Elsevier. This book was released on 2024-05-05 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: Metaheuristic Optimization Algorithms: Optimizers, Analysis, and Applications presents the most recent optimization algorithms and their applications across a wide range of scientific and engineering research fields. Metaheuristic Optimization Algorithms have become indispensable tools, with applications in data analysis, text mining, classification problems, computer vision, image analysis, pattern recognition, medicine, and many others. Most complex systems problems involve a continuous flow of data that makes it impossible to manage and analyze manually. The outcome depends on the processing of high-dimensional data, most of it irregular and unordered, present in various forms such as text, images, videos, audio, and graphics. The authors of Meta-Heuristic Optimization Algorithms provide readers with a comprehensive overview of eighteen optimization algorithms to address this complex data, including Particle Swarm Optimization Algorithm, Arithmetic Optimization Algorithm, Whale Optimization Algorithm, and Marine Predators Algorithm, along with new and emerging methods such as Aquila Optimizer, Quantum Approximate Optimization Algorithm, Manta-Ray Foraging Optimization Algorithm, and Gradient Based Optimizer, among others. Each chapter includes an introduction to the modeling concepts used to create the algorithm, followed by the mathematical and procedural structure of the algorithm, associated pseudocode, and real-world case studies to demonstrate how each algorithm can be applied to a variety of scientific and engineering solutions. World-renowned researchers and practitioners in Metaheuristics present the procedures and pseudocode for creating a wide range of optimization algorithms Helps readers formulate and design the best optimization algorithms for their research goals through case studies in a variety of real-world applications Helps readers understand the links between Metaheuristic algorithms and their application in Computational Intelligence, Machine Learning, and Deep Learning problems

Download or read book Advances and Applications of Optimised Algorithms in Image Processing written by Diego Oliva and published by Springer. This book was released on 2016-11-21 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a study of the use of optimization algorithms in complex image processing problems. The problems selected explore areas ranging from the theory of image segmentation to the detection of complex objects in medical images. Furthermore, the concepts of machine learning and optimization are analyzed to provide an overview of the application of these tools in image processing. The material has been compiled from a teaching perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics, and can be used for courses on Artificial Intelligence, Advanced Image Processing, Computational Intelligence, etc. Likewise, the material can be useful for research from the evolutionary computation, artificial intelligence and image processing communities.

Download or read book Metaheuristics in Machine Learning Theory and Applications written by Diego Oliva and published by Springer Nature. This book was released on with total page 765 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and implementations are included; in this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and is useful in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the book is useful for research from the evolutionary computation, artificial intelligence, and image processing communities.

Download or read book Data Clustering and Image Segmentation Through Genetic Algorithms written by Sujata Dash and published by Engineering Science Reference. This book was released on 2018-08-03 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides a broad overview of genetic algorithms, clustering algorithms influenced by genetic algorithms, improvements attained in the field of image segmentation and their application by using genetic algorithms. It also explores the comparative analysis of earlier methods and the recent ones proposed with the use of genetic algorithms"--

Download or read book Metaheuristic Algorithms written by Gai-Ge Wang and published by CRC Press. This book was released on 2024-04-03 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces the theory and applications of metaheuristic algorithms. It also provides methods for solving practical problems in such fields as software engineering, image recognition, video networks, and in the oceans. In the theoretical section, the book introduces the information feedback model, learning-based intelligent optimization, dynamic multi-objective optimization, and multi-model optimization. In the applications section, the book presents applications of optimization algorithms to neural architecture search, fuzz testing, oceans, and image processing. The neural architecture search chapter introduces the latest NAS method. The fuzz testing chapter uses multi-objective optimization and ant colony optimization to solve the seed selection and energy allocation problems in fuzz testing. In the ocean chapter, deep learning methods such as CNN, transformer, and attention-based methods are used to describe ENSO prediction and image processing for marine fish identification, and to provide an overview of traditional classification methods and deep learning methods. Rich in examples, this book will be a great resource for students, scholars, and those interested in metaheuristic algorithms, as well as professional practitioners and researchers working on related topics.

Download or read book Machine Learning and Metaheuristics Methods and Analysis written by Uma N. Dulhare and published by Springer Nature. This book was released on 2023-12-03 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book takes a balanced approach between theoretical understanding and real-time applications. All the topics included real-world problems which show how to explore, build, evaluate, and optimize machine learning models fusion with metaheuristic algorithms. Optimization algorithms classified into two broad categories as deterministic and probabilistic algorithms. The content of book elaborates optimization algorithms such as particle swarm optimization, ant colony optimization, whale search algorithm, and cuckoo search algorithm.

Download or read book Metaheuristics and Optimization in Computer and Electrical Engineering written by Navid Razmjooy and published by Springer Nature. This book was released on 2020-11-16 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of artificial intelligence, especially in the field of optimization is increasing day by day. The purpose of this book is to explore the possibility of using different kinds of optimization algorithms to advance and enhance the tools used for computer and electrical engineering purposes.

Download or read book Metaheuristics and Optimization in Computer and Electrical Engineering written by Navid Razmjooy and published by Springer Nature. This book was released on 2023-11-08 with total page 491 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.