A Beginner S Guide To Multilevel Image Thresholding

"Choosing appropriate thresholding based on the existing image (Grayscale/RGB) and the assigned task is essential to attain better result and the complexity of the image thresholding is largely reduced with the help of a chosen heuristic algorithm technique. This book emphasizes various image thresholding methods that are necessary for image pre-processing and initial level enhancement. It presents a detailed evaluation, such as choosing the threshold function, need for the heuristic algorithm, choice of objective function, and evaluation of the outcome along with real-time applications in detail"--

A Beginner’s Guide to Image Multi-Level Thresholding emphasizes various image thresholding methods that are necessary for image pre-processing and initial level enhancement. Explains basic concepts and the implementation of Image Multi-Level Thresholding (grayscale and RGB images) Presents a detailed evaluation in real-time application, including the need for heuristic algorithm, the choice of objective and threshold function, and the evaluation of the outcome Describes how the image thresholding acts as a pre-processing technique and how the region of interest in a medical image is enhanced with thresholding Illustrates integration of the thresholding technique with bio-inspired algorithms Includes current findings and future directions of image multi-level thresholding and its practical implementation Emphasizes the need for multi-level thresholding with suitable examples The book is aimed at graduate students and researchers in image processing, electronics engineering, computer sciences and engineering.

A Beginner's Guide to Image Multi-Level Thresholding emphasizes various image thresholding methods that are necessary for image pre-processing and initial level enhancement. Explains basic concepts and the implementation of Image Multi-Level Thresholding (grayscale and RGB images) Presents a detailed evaluation in real-time application, including the need for heuristic algorithm, the choice of objective and threshold function, and the evaluation of the outcome Describes how the image thresholding acts as a pre-processing technique and how the region of interest in a medical image is enhanced with thresholding Illustrates integration of the thresholding technique with bio-inspired algorithms Includes current findings and future directions of image multi-level thresholding and its practical implementation Emphasizes the need for multi-level thresholding with suitable examples The book is aimed at graduate students and researchers in image processing, electronics engineering, computer sciences and engineering.

Magnetic Resonance Imaging: Recording, Reconstruction and Assessment gives a detailed overview of magnetic resonance imaging (MRI), along with its applications and challenges. The book explores the abnormalities in internal human organs using MRI techniques while also featuring case studies that illustrate measures used. In addition, it explores precautionary measures used during MRI based imaging, the selection of appropriate contrast agents, and the selection of the appropriate modality during the image registration. Sections introduce medical imaging, the use of MRI in brain, cardiac, lung and kidney detection, and also discuss both 2D and 3D imaging techniques and various MRI modalities. This volume will be of interest to researchers, engineers and medical professionals involved in the development and use of MRI systems. Discusses challenges and issues faced, as well as safety precautions to be followed Features case studies with benchmark MRIs existing in the literature Introduces computer-based assessment (Machine Learning and Deep Learning) of the MRI based on its 2D slices

VANET (vehicular ad hoc network) is a subgroup of MANET (mobile ad hoc network). It enables communication among vehicles on the road and between related infrastructures. This book addresses the basic elements of VANET along with components involved in the communication with their functionalities and configurations. It contains numerous examples, case studies, technical descriptions, scenarios, procedures, algorithms, and protocols, and addresses the different services provided by VANET with the help of a scenario showing a network tackling an emergency. Features: • Covers all important concepts of VANET for beginners and different road scenarios in VANET • Covers essential communication protocols in VANET • Introduces approaches for VANET implementation using simulators • Provides a classification of messages and a priority-based message forwarding strategy This book is aimed at undergraduates, postgraduates, industry, researchers, and research scholars in information and communications technology.

This book features selected papers presented at International Conference on Data Analytics and Computing (ICDAC 2022), organized by Department of Mathematics, College of Science and Technology, Wenzhou-Kean University, Wenzhou, China, held during May 28–29, 2022. This book includes state-of-the-art current trends in data science, data analytics optimization, soft computing and related areas. Its primary readers are postgraduate students, researchers and academic professionals.

Nature-inspired algorithms such as cuckoo search and firefly algorithm have become popular and widely used in recent years in many applications. These algorithms are flexible, efficient and easy to implement. New progress has been made in the last few years, and it is timely to summarize the latest developments of cuckoo search and firefly algorithm and their diverse applications. This book will review both theoretical studies and applications with detailed algorithm analysis, implementation and case studies so that readers can benefit most from this book. Application topics are contributed by many leading experts in the field. Topics include cuckoo search, firefly algorithm, algorithm analysis, feature selection, image processing, travelling salesman problem, neural network, GPU optimization, scheduling, queuing, multi-objective manufacturing optimization, semantic web service, shape optimization, and others. This book can serve as an ideal reference for both graduates and researchers in computer science, evolutionary computing, machine learning, computational intelligence, and optimization, as well as engineers in business intelligence, knowledge management and information technology.

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.