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Image Processing and Capsule Networks: ICIPCN 2020 (Advances in Intelligent Systems and Computing #1200)
by João Manuel R. S. Tavares Joy Iong-Zong Chen Subarna Shakya Abdullah M. IliyasuThis book emphasizes the emerging building block of image processing domain, which is known as capsule networks for performing deep image recognition and processing for next-generation imaging science. Recent years have witnessed the continuous development of technologies and methodologies related to image processing, analysis and 3D modeling which have been implemented in the field of computer and image vision. The significant development of these technologies has led to an efficient solution called capsule networks [CapsNet] to solve the intricate challenges in recognizing complex image poses, visual tasks, and object deformation. Moreover, the breakneck growth of computation complexities and computing efficiency has initiated the significant developments of the effective and sophisticated capsule network algorithms and artificial intelligence [AI] tools into existence. The main contribution of this book is to explain and summarize the significant state-of-the-art research advances in the areas of capsule network [CapsNet] algorithms and architectures with real-time implications in the areas of image detection, remote sensing, biomedical image analysis, computer communications, machine vision, Internet of things, and data analytics techniques.
Image Processing and Communications Challenges 10: 10th International Conference, IP&C’2018 Bydgoszcz, Poland, November 2018, Proceedings (Advances in Intelligent Systems and Computing #892)
by Ryszard S. Choraś Michał ChoraśPresenting a collection of high-quality research papers on image processing and communications, this book not only discusses emerging applications of the currently available solutions, but also outlines potential future techniques and research directions in these areas. Gathering the proceedings of the 10th International Conference on Image Processing and Communications (IP&C 2018), held in Bydgoszcz, Poland in November 2018, it is divided into two parts. Part I focuses on image processing, offering a comprehensive survey of available methods and discussing current trends in computer vision. In turn, Part II presents novel results on networks, communications and a diverse range of applications, including cybersecurity and cloud computing.
Image Processing and Communications Challenges 7
by Ryszard S. ChoraśThis book contains papers accepted for IP&C 2015, the International Conference on Image Processing and Communications, held at UTP University of Science and Technology, Bydgoszcz, Poland, September 9-11, 2015. This conference was the eighth edition in the IP&C series of annual conferences. This book and the conference have the aim to bring together researchers and scientists in the broad fields of image processing and communications, addressing recent advances in theory, methodology and applications. The book will be of interest to a large group of researchers, engineers and practitioners in image processing and communications.
Image Processing and Communications Challenges 8
by Ryszard S. ChoraśThis book collects a series of research papers in the area of Image Processing and Communications which not only introduce a summary of current technology but also give an outlook of potential feature problems in this area. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in image processing and communications. The book is divided into two parts and presents the proceedings of the 8th International Image Processing and Communications Conference (IP&C 2016) held in Bydgoszcz, Poland September 7-9 2016. Part I deals with image processing. A comprehensive survey of different methods of image processing, computer vision is also presented. Part II deals with the telecommunications networks and computer networks. Applications in these areas are considered.
Image Processing and Communications: Techniques, Algorithms and Applications (Advances in Intelligent Systems and Computing #1062)
by Ryszard S. Choraś Michał ChoraśThis book presents a selection of high-quality peer-reviewed research papers on various aspects of computer science and networks. It not only discusses emerging applications of currently available solutions, but also outlines potential future techniques and lines of research in pattern recognition, image processing and communications. Given its scope, the book will be of considerable interest to researchers, students and practitioners alike. All papers gathered here were presented at the Image Processing and Communications Conference, held in Bydgoszcz, Poland on September 11–13, 2019.
Image Processing and Computer Vision in iOS (SpringerBriefs in Computer Science)
by Oge MarquesThis book presents the fundamentals of mobile visual computing in iOS development and provides directions for developers and researchers interested in developing iOS applications with image processing and computer vision capabilities. Presenting a technical overview of some of the tools, languages, libraries, frameworks, and APIs currently available for developing iOS applications Image Processing and Computer Vision in iOS reveals the rich capabilities in image processing and computer vision. Its main goal is to provide a road map to what is currently available, and a path to successfully tackle this rather complex but highly rewarding task.
Image Processing and Intelligent Computing Systems
by Abhishek Verma Prabhat Kumar Srivastava Prateek Singhal Ram Kumar Virender RangaThere is presently a drastic growth in multimedia data. During the Covid-19 pandemic, we observed that images helped doctors immensely in the rapid detection of Covid-19 infection in patients. There are many critical applications in which images play a vital role. These applications use raw image data to extract some useful information about the world around us. The quick extraction of valuable information from raw images is one challenge that academicians and professionals face in the present day. This is where image processing comes into action. Image processing’s primary purpose is to get an enhanced image or extract some useful information from raw image data. Therefore, there is a major need for some technique or system that addresses this challenge. Intelligent Systems have emerged as a solution to address quick image information extraction. In simple words, an Intelligent System can be defined as a mathematical model that adapts itself to deal with a problem’s dynamicity. These systems learn how to act so an image can reach an objective. An Intelligent System helps accomplish various image-processing functions like enhancement, segmentation, reconstruction, object detection, and morphing. The advent of Intelligent Systems in the image-processing field has leveraged many critical applications for humankind. These critical applications include factory automation, biomedical imaging analysis, decision econometrics, as well as related challenges.
Image Processing and Machine Learning, Volume 1: Foundations of Image Processing
by Erik Cuevas Alma Nayeli RodríguezImage processing and machine learning are used in conjunction to analyze and understand images. Where image processing is used to pre-process images using techniques such as filtering, segmentation, and feature extraction, machine learning algorithms are used to interpret the processed data through classification, clustering, and object detection. This book serves as a textbook for students and instructors of image processing, covering the theoretical foundations and practical applications of some of the most prevalent image processing methods and approaches. Divided into two volumes, this first installment explores the fundamental concepts and techniques in image processing, starting with pixel operations and their properties and exploring spatial filtering, edge detection, image segmentation, corner detection, and geometric transformations. It provides a solid foundation for readers interested in understanding the core principles and practical applications of image processing, establishing the essential groundwork necessary for further explorations covered in Volume 2. Written with instructors and students of image processing in mind, this book’s intuitive organization also contains appeal for app developers and engineers.
Image Processing and Machine Learning, Volume 2: Advanced Topics in Image Analysis and Machine Learning
by Erik Cuevas Alma Nayeli RodríguezImage processing and machine learning are used in conjunction to analyze and understand images. Where image processing is used to pre-process images using techniques such as filtering, segmentation, and feature extraction, machine learning algorithms are used to interpret the processed data through classification, clustering, and object detection. This book serves as a textbook for students and instructors of image processing, covering the theoretical foundations and practical applications of some of the most prevalent image processing methods and approaches. Divided into two volumes, this second installment explores the more advanced concepts and techniques in image processing, including morphological filters, color image processing, image matching, feature-based segmentation utilizing the mean shift algorithm, and the application of singular value decomposition for image compression. This second volume also incorporates several important machine learning techniques applied to image processing, building on the foundational knowledge introduced in Volume 1. Written with instructors and students of image processing in mind, this book’s intuitive organization also contains appeal for app developers and engineers.
Image Processing and Pattern Recognition Based on Parallel Shift Technology
by Stepan Bilan Sergey YuzhakovThis book describes the methods and algorithms for image pre-processing and recognition. These methods are based on a parallel shift technology of the imaging copy, as well as simple mathematical operations to allow the generation of a minimum set of features to describe and recognize the image. This book also describes the theoretical foundations of parallel shift technology and pattern recognition. Based on these methods and theories, this book is intended to help researchers with artificial intelligence systems design, robotics, and developing software and hardware applications.
Image Processing for Cinema (Chapman & Hall/CRC Mathematical and Computational Imaging Sciences Series)
by Marcelo BertalmioImage Processing for Cinema presents a detailed overview of image processing techniques that are used in practice in digital cinema. The book shows how image processing has become ubiquitous in movie-making, from shooting to exhibition. It covers all the ways in which image processing algorithms are used to enhance, restore, adapt, and convert movi
Image Processing in Diabetic Related Causes
by Amit Kumar Fahimuddin ShaikThis book is a collection of all the experimental results and analysis carried out on medical images of diabetic related causes. The experimental investigations have been carried out on images starting from very basic image processing techniques such as image enhancement to sophisticated image segmentation methods. This book is intended to create an awareness on diabetes and its related causes and image processing methods used to detect and forecast in a very simple way. This book is useful to researchers, Engineers, Medical Doctors and Bioinformatics researchers.
Image Processing using Pulse-Coupled Neural Networks: Applications in Python
by Thomas Lindblad Jason M. KinserImage processing algorithms based on the mammalian visual cortex are powerful tools for extraction information and manipulating images. This book reviews the neural theory and translates them into digital models. Applications are given in areas of image recognition, foveation, image fusion and information extraction. The third edition reflects renewed international interest in pulse image processing with updated sections presenting several newly developed applications. This edition also introduces a suite of Python scripts that assist readers in replicating results presented in the text and to further develop their own applications.
Image Processing with Cellular Topology
by Vladimir KovalevskyThis book explains why the finite topological space known as abstract cell complex is important for successful image processing and presents image processing methods based on abstract cell complex, especially for tracing and encoding of boundaries of homogeneous regions. Many examples are provided in the book, some teach you how to trace and encode boundaries in binary, indexed and colour images. Other examples explain how to encode a boundary as a sequence of straight-line segments which is important for shape recognition. A new method of edge detection in two- and three-dimensional images is suggested. Also, a discussion problem is included in the book: A derivative is defined as the limit of the relation of the increment of the function to the increment of the argument as the latter tends to zero. Is it not better to estimate derivatives as the relation of the increment of the function to the optimal increment of the argument instead of taking exceedingly small increment which leads to errors? This book addresses all above questions and provide the answers.
Image Processing with ImageJ
by Javier Pascau Jose Maria Mateos-PerezThe book will help readers discover the various facilities of ImageJ through a tutorial-based approach.This book is targeted at scientists, engineers, technicians, and managers, and anyone who wishes to master ImageJ for image viewing, processing, and analysis. If you are a developer, you will be able to code your own routines after you have finished reading this book. No prior knowledge of ImageJ is expected.
Image Processing with ImageJ - Second Edition
by Javier Pascau Jurjen Broeke Jose Maria PerezExtract and analyze data from complex images with ImageJ, the world's leading image processing tool About This Book * Design automated image-processing solutions and speed up image-processing tasks with ImageJ * Create quality and intuitive interfaces for image processing by developing a basic framework for ImageJ plugins. * Tackle even the most sophisticated datasets and complex images Who This Book Is For The book has been created for engineers, scientists, and developers eager to tackle image processing with one of the leading tools available. No prior knowledge of ImageJ is needed. Familiarity with Java programming will be required for readers to code their own routines using ImageJ. What You Will Learn * Install and set up ImageJ for image processing. * Process images using ImageJ's built-in tools * Create macros to perform repetitive processing tasks * Set up and use an integrated development environment for ImageJ plugins * Create plugins with a user-friendly interface for processing * Use established ImageJ plugins for processing and quantification * Generate a simple interface based on a real world example and create other interfaces for other projects * Speed up interface development by setting multiple parameters interactively In Detail Advances in image processing have been vital for the scientific and technological communities, making it possible to analyze images in greater detail than ever before. But as images become larger and more complex, advanced processing techniques are required. ImageJ is built for the modern challenges of image processing - it's one of the key tools in its development, letting you automate basic tasks so you can focus on sophisticated, in depth analysis. This book demonstrates how to put ImageJ into practice. It outlines its key features and demonstrates how to create your own image processing applications using macros and ImageJ plugins. Once you've got to grips with the basics of ImageJ, you'll then discover how to build a number of different image processing solutions. From simple tasks to advanced and automated image processing, you'll gain confidence with this innovative and powerful tool - however and whatever you are using it for. Style and approach A step-by-step guide to image processing and developing macros and plugins in ImageJ. The book will progress from using the built-in tools to macros and finally plugins for image processing.
Image Processing with MATLAB: Applications in Medicine and Biology
by Omer Demirkaya Musa H. Asyali Prasanna K. SahooImage Processing with MATLAB: Applications in Medicine and Biology explains complex, theory-laden topics in image processing through examples and MATLAB algorithms. It describes classical as well emerging areas in image processing and analysis. Providing many unique MATLAB codes and functions throughout, the book covers the theory of probability an
Image Processing, Computer Vision, and Pattern Recognition and Information and Knowledge Engineering: 28th International Conference, IPCV 2024, and 23rd International Conference, IKE 2024, Held as Part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024, Las Vegas, NV, USA, July 22–25, 2024, Revised Selected Papers (Communications in Computer and Information Science #2262)
by Hamid R. Arabnia Leonidas Deligiannidis Soheyla Amirian Farzan Shenavarmasouleh Farid Ghareh MohammadiThis book constitutes the proceedings of the 28th International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2024, and the 23rd International Conference on Information and Knowledge Engineering, IKE 2024, held as part of the 2024 World Congress in Computer Science, Computer Engineering and Applied Computing, in Las Vegas, USA, during July 22 to July 25, 2024. The 19 IPCV 2024 papers included in these proceedings were carefully reviewed and selected from 98 submissions. IKE 2024 received 40 submissions and accepted 10 papers for inclusion in the proceedings. The papers have been organized in topical sections as follows: Image processing, computer vision and pattern recognition; image processing, computer vision and pattern recognition - detection methods; and information and knowledge engineering.
Image Processing: Dealing with Texture
by Maria M. Petrou Sei-ichiro KamataThe classic text that covers practical image processing methods and theory for image texture analysis, updated second edition The revised second edition of Image Processing: Dealing with Textures updates the classic work on texture analysis theory and methods without abandoning the foundational essentials of this landmark work. Like the first, the new edition offers an analysis of texture in digital images that are essential to a diverse range of applications such as: robotics, defense, medicine and the geo-sciences. Designed to easily locate information on specific problems, the text is structured around a series of helpful questions and answers. Updated to include the most recent developments in the field, many chapters have been completely revised including: Fractals and Multifractals, Image Statistics, Texture Repair, Local Phase Features, Dual Tree Complex Wavelet Transform, Ridgelets and Curvelets and Deep Texture Features. The book takes a two-level mathematical approach: light math is covered in the main level of the book, with harder math identified in separate boxes. This important text: Contains an update of the classic advanced text that reviews practical image processing methods and theory for image texture analysis Puts the focus exclusively on an in-depth exploration of texture Contains a companion website with exercises and algorithms Includes examples that are fully worked to enhance the learning experience Written for students and researchers of image processing, the second edition of Image Processing has been revised and updated to incorporate the foundational information on the topic and information on the latest advances.
Image Quality Assessment of Computer-generated Images: Based On Machine Learning And Soft Computing (SpringerBriefs in Computer Science)
by André Bigand Julien Dehos Christophe Renaud Joseph ConstantinImage Quality Assessment is well-known for measuring the perceived image degradation of natural scene images but is still an emerging topic for computer-generated images. This book addresses this problem and presents recent advances based on soft computing. It is aimed at students, practitioners and researchers in the field of image processing and related areas such as computer graphics and visualization. In this book, we first clarify the differences between natural scene images and computer-generated images, and address the problem of Image Quality Assessment (IQA) by focusing on the visual perception of noise. Rather than using known perceptual models, we first investigate the use of soft computing approaches, classically used in Artificial Intelligence, as full-reference and reduced-reference metrics. Thus, by creating Learning Machines, such as SVMs and RVMs, we can assess the perceptual quality of a computer-generated image. We also investigate the use of interval-valued fuzzy sets as a no-reference metric. These approaches are treated both theoretically and practically, for the complete process of IQA. The learning step is performed using a database built from experiments with human users and the resulting models can be used for any image computed with a stochastic rendering algorithm. This can be useful for detecting the visual convergence of the different parts of an image during the rendering process, and thus to optimize the computation. These models can also be extended to other applications that handle complex models, in the fields of signal processing and image processing.
Image Recognition and Classification: Algorithms, Systems, and Applications (Optical Science and Engineering)
by Bahram Javidi"Details the latest image processing algorithms and imaging systems for image recognition with diverse applications to the military; the transportation, aerospace, information security, and biomedical industries; radar systems; and image tracking systems."
Image Registration
by A. Ardeshir GoshtasbyThis book presents a thorough and detailed guide to image registration, outlining the principles and reviewing state-of-the-art tools and methods. The book begins by identifying the components of a general image registration system, and then describes the design of each component using various image analysis tools. The text reviews a vast array of tools and methods, not only describing the principles behind each tool and method, but also measuring and comparing their performances using synthetic and real data. Features: discusses similarity/dissimilarity measures, point detectors, feature extraction/selection and homogeneous/heterogeneous descriptors; examines robust estimators, point pattern matching algorithms, transformation functions, and image resampling and blending; covers principal axes methods, hierarchical methods, optimization-based methods, edge-based methods, model-based methods, and adaptive methods; includes a glossary, an extensive list of references, and an appendix on PCA.
Image Restoration: Fundamentals and Advances (Digital Imaging and Computer Vision #7)
by Xin Li Bahadir K. GunturkImage Restoration: Fundamentals and Advances responds to the need to update most existing references on the subject, many of which were published decades ago. Providing a broad overview of image restoration, this book explores breakthroughs in related algorithm development and their role in supporting real-world applications associated with various scientific and engineering fields. These include astronomical imaging, photo editing, and medical imaging, to name just a few. The book examines how such advances can also lead to novel insights into the fundamental properties of image sources. Addressing the many advances in imaging, computing, and communications technologies, this reference strikes just the right balance of coverage between core fundamental principles and the latest developments in this area. Its content was designed based on the idea that the reproducibility of published works on algorithms makes it easier for researchers to build on each other’s work, which often benefits the vitality of the technical community as a whole. For that reason, this book is as experimentally reproducible as possible. Topics covered include: Image denoising and deblurring Different image restoration methods and recent advances such as nonlocality and sparsity Blind restoration under space-varying blur Super-resolution restoration Learning-based methods Multi-spectral and color image restoration New possibilities using hybrid imaging systems Many existing references are scattered throughout the literature, and there is a significant gap between the cutting edge in image restoration and what we can learn from standard image processing textbooks. To fill that need but avoid a rehash of the many fine existing books on this subject, this reference focuses on algorithms rather than theories or applications. Giving readers access to a large amount of downloadable source code, the book illustrates fundamental techniques, key ideas developed over the years, and the state of the art in image restoration. It is a valuable resource for readers at all levels of understanding.
Image Segmentation: Principles, Techniques, and Applications
by Asoke K. Nandi Tao LeiImage Segmentation Summarizes and improves new theory, methods, and applications of current image segmentation approaches, written by leaders in the field The process of image segmentation divides an image into different regions based on the characteristics of pixels, resulting in a simplified image that can be more efficiently analyzed. Image segmentation has wide applications in numerous fields ranging from industry detection and bio-medicine to intelligent transportation and architecture. Image Segmentation: Principles, Techniques, and Applications is an up-to-date collection of recent techniques and methods devoted to the field of computer vision. Covering fundamental concepts, new theories and approaches, and a variety of practical applications including medical imaging, remote sensing, fuzzy clustering, and watershed transform. In-depth chapters present innovative methods developed by the authors—such as convolutional neural networks, graph convolutional networks, deformable convolution, and model compression—to assist graduate students and researchers apply and improve image segmentation in their work. Describes basic principles of image segmentation and related mathematical methods such as clustering, neural networks, and mathematical morphology. Introduces new methods for achieving rapid and accurate image segmentation based on classic image processing and machine learning theory. Presents techniques for improved convolutional neural networks for scene segmentation, object recognition, and change detection, etc. Highlights the effect of image segmentation in various application scenarios such as traffic image analysis, medical image analysis, remote sensing applications, and material analysis, etc. Image Segmentation: Principles, Techniques, and Applications is an essential resource for undergraduate and graduate courses such as image and video processing, computer vision, and digital signal processing, as well as researchers working in computer vision and image analysis looking to improve their techniques and methods.
Image Statistics in Visual Computing
by Tania Pouli Erik Reinhard Douglas W. CunninghamTo achieve the complex task of interpreting what we see, our brains rely on statistical regularities and patterns in visual data. Knowledge of these regularities can also be considerably useful in visual computing disciplines, such as computer vision, computer graphics, and image processing. The field of natural image statistics studies the regular