This book presents practical optimization techniques used in image processing and computer vision problems. Ill-posed problems are introduced and used as examples to show how each type of problem is related to typical image processing and computer vision problems. Unconstrained optimization gives the best solution based on numerical minimization of a single, scalar-valued objective function or cost function. Unconstrained optimization problems have been intensively studied, and many algorithms and tools have been developed to solve them. Most practical optimization problems, however, arise with a set of constraints. Typical examples of constraints include: (i) pre-specified pixel intensity range, (ii) smoothness or correlation with neighboring information, (iii) existence on a certain contour of lines or curves, and (iv) given statistical or spectral characteristics of the solution. Regularized optimization is a special method used to solve a class of constrained optimization problems. The term regularization refers to the transformation of an objective function with constraints into a different objective function, automatically reflecting constraints in the unconstrained minimization process. Because of its simplicity and efficiency, regularized optimization has many application areas, such as image restoration, image reconstruction, optical flow estimation, etc. Optimization plays a major role in a wide variety of theories for image processing and computer vision. Various optimization techniques are used at different levels for these problems, and this volume summarizes and explains these techniques as applied to image processing and computer vision.

This book presents novel hybrid encryption algorithms that possess many different characteristics. In particular, "Hybrid Encryption Algorithms over Wireless Communication Channels", examines encrypted image and video data for the purpose of secure wireless communications. A study of two different families of encryption schemes are introduced: namely, permutation-based and diffusion-based schemes. The objective of the book is to help the reader selecting the best suited scheme for the transmission of encrypted images and videos over wireless communications channels, with the aid of encryption and decryption quality metrics. This is achieved by applying number-theory based encryption algorithms, such as chaotic theory with different modes of operations, the Advanced Encryption Standard (AES), and the RC6 in a pre-processing step in order to achieve the required permutation and diffusion. The Rubik's cube is used afterwards in order to maximize the number of permutations. Transmission of images and videos is vital in today's communications systems. Hence, an effective encryption and modulation schemes are a must. The author adopts Orthogonal Frequency Division Multiplexing (OFDM), as the multicarrier transmission choice for wideband communications. For completeness, the author addresses the sensitivity of the encrypted data to the wireless channel impairments, and the effect of channel equalization on the received images and videos quality. Complete simulation experiments with MATLAB (R) codes are included. The book will help the reader obtain the required understanding for selecting the suitable encryption method that best fulfills the application requirements.

This book presents essential perspectives on digital convolutions in wireless communications systems and illustrates their corresponding efficient real-time field-programmable gate array (FPGA) implementations. FPGAs or generic all programmable devices will soon become widespread, serving as the "brains" of all types of real-time smart signal processing systems, like smart networks, smart homes and smart cities. The book examines digital convolution by bringing together the following main elements: the fundamental theory behind the mathematical formulae together with corresponding physical phenomena; virtualized algorithm simulation together with benchmark real-time FPGA implementations; and detailed, state-of-the-art case studies on wireless applications, including popular linear convolution in digital front ends (DFEs); nonlinear convolution in digital pre-distortion (DPD) enabled high-efficiency wireless RF transceivers; and fast linear convolution in massive multiple-input multiple-output (MIMO) systems. After reading this book, students and professionals will be able to: * Understand digital convolution with inside-out information: discover what convolution is, why it is important and how it works. * Enhance their FPGA design skills, i.e., enhance their FPGA-related prototyping capability with model-based hands-on examples. * Rapidly expand their digital signal processing (DSP) blocks: to examine how to rapidly and efficiently create (DSP) functional blocks on a programmable FPGA chip as a reusable intellectual property (IP) core. * Upgrade their expertise as both "thinkers" and "doers": minimize/close the gap between mathematical equations and FPGA implementations for existing and emerging wireless applications.

With annual gross sales surpassing 100 billion U.S. dollars each of the last two years, the digital games industry may one day challenge theatrical-release movies as the highest-grossing entertainment media in the world. In their examination of the tremendous cultural influence of digital games, Daniel Reardon and David Wright analyze three companies that have shaped the industry: Bethesda, located in Rockville, Maryland, USA; BioWare in Edmonton, Alberta, Canada; and CD Projekt Red in Warsaw, Poland. Each company has used social media and technical content in the games to promote players' belief that players control the companies' game narratives. The result has been at times explosive, as empowered players often attempted to co-op the creative processes of games through discussion board forum demands, fund-raising campaigns to persuade companies to change or add game content, and modifications ("modding") of the games through fan-created downloads. The result has changed the way we understand the interactive nature of digital games and the power of fan culture to shape those games.

This book provides a systematic overview of watermarking and steganography methods for triangle meshes related to computer graphics and security. The significance of this research has been well recognized by the growing body of work on watermarking, steganography and steganalysis of 3D meshes. With the evolution of the CAD industry and real-world end-user applications such as virtual reality (VR) and 3D printing, 3D meshes have attracted world-wide attention. Besides, the flexible data structure of 3D geometry provides enough space to embed secret information, making it ideal for applications such as copyright protection and covert communication. Our goal of the book is to allow readers to systematically understand 3D mesh information hiding technology and its applications as a whole. The book outlines comprehensive techniques, including handcrafted and deep learning-based techniques, digital and physical techniques in the literature and provides standard evaluation metrics for triangle meshes. The up-to-date geometrical deep learning and 3D printing-related algorithms are also covered. Offering a rich blend of ideas and algorithms, the book is up-to-date and self-contained. It is an excellent reference resource for users who are seeking 3D mesh watermarking and steganography algorithms, as well as for graduate students and researchers wanting to grasp the frontiers of triangular mesh processing on data hiding.

a balanced blend of theoretical and practical information that enables readers to develop 3D worlds quickly and efficiently.

This book presents non-linear image enhancement approaches to mammograms as a robust computer-aided analysis solution for the early detection of breast cancer, and provides a compendium of non-linear mammogram enhancement approaches: from the fundamentals to research challenges, practical implementations, validation, and advances in applications. The book includes a comprehensive discussion on breast cancer, mammography, breast anomalies, and computer-aided analysis of mammograms. It also addresses fundamental concepts of mammogram enhancement and associated challenges, and features a detailed review of various state-of-the-art approaches to the enhancement of mammographic images and emerging research gaps. Given its scope, the book offers a valuable asset for radiologists and medical experts (oncologists), as mammogram visualization can enhance the precision of their diagnostic analyses; and for researchers and engineers, as the analysis of non-linear filters is one of the most challenging research domains in image processing.

This book comprises a collection of papers by international experts, presented at the International Conference on NextGen Electronic Technologies (ICNETS2-2017). ICNETS2 encompassed six symposia covering all aspects of electronics and communications engineering domains, including relevant nano/micro materials and devices. Featuring the latest research on computational signal processing and analysis, the book is useful to researchers, professionals, and students working in the core areas of electronics and their applications, especially signal processing, embedded systems, and networking.

Images are all around us The proliferation of low-cost, high-quality imaging devices has led to an explosion in acquired images. When these images are acquired from a microscope, telescope, satellite, or medical imaging device, there is a statistical image processing task: the inference of something--an artery, a road, a DNA marker, an oil spill--from imagery, possibly noisy, blurry, or incomplete. A great many textbooks have been written on image processing. However this book does not so much focus on images, per se, but rather on spatial data sets, with one or more measurements taken over a two or higher dimensional space, and to which standard image-processing algorithms may not apply. There are many important data analysis methods developed in this text for such statistical image problems. Examples abound throughout remote sensing (satellite data mapping, data assimilation, climate-change studies, land use), medical imaging (organ segmentation, anomaly detection), computer vision (image classification, segmentation), and other 2D/3D problems (biological imaging, porous media). The goal, then, of this text is to address methods for solving multidimensional statistical problems. The text strikes a balance between mathematics and theory on the one hand, versus applications and algorithms on the other, by deliberately developing the basic theory (Part I), the mathematical modeling (Part II), and the algorithmic and numerical methods (Part III) of solving a given problem. The particular emphases of the book include inverse problems, multidimensional modeling, random fields, and hierarchical methods.

Tensor signal processing is an emerging field with important applications to computer vision and image processing.

This book presents the state of the art in this new branch of signal processing, offering a great deal of research and discussions by leading experts in the area. The wide-ranging volume offers an overview into cutting-edge research into the newest tensor processing techniques and their application to different domains related to computer vision and image processing.

This comprehensive text will prove to be an invaluable reference and resource for researchers, practitioners and advanced students working in the area of computer vision and image processing.

This book presents studies involving algorithms in the machine learning paradigms. It discusses a variety of learning problems with diverse applications, including prediction, concept learning, explanation-based learning, case-based (exemplar-based) learning, statistical rule-based learning, feature extraction-based learning, optimization-based learning, quantum-inspired learning, multi-criteria-based learning and hybrid intelligence-based learning.

This book discusses blind investigation and recovery of digital evidence left behind on digital devices, primarily for the purpose of tracing cybercrime sources and criminals. It presents an overview of the challenges of digital image forensics, with a specific focus on two of the most common forensic problems. The first part of the book addresses image source investigation, which involves mapping an image back to its camera source to facilitate investigating and tracing the source of a crime. The second part of the book focuses on image-forgery detection, primarily focusing on "copy-move forgery" in digital images, and presenting effective solutions to copy-move forgery detection with an emphasis on additional related challenges such as blur-invariance, similar genuine object identification, etc. The book concludes with future research directions, including counter forensics. With the necessary mathematical information in every chapter, the book serves as a useful reference resource for researchers and professionals alike. In addition, it can also be used as a supplementary text for upper-undergraduate and graduate-level courses on "Digital Image Processing", "Information Security", "Machine Learning", "Computer Vision" and "Multimedia Security and Forensics".

This book continues first one of the same authors "Adaptive Image Processing Algorithms for Printing" and presents methods and software solutions for copying and scanning various types of documents by conventional office equipment, offering techniques for correction of distortions and enhancement of scanned documents; techniques for automatic cropping and de-skew; approaches for segmentation of text and picture regions; documents classifiers; approach for vectorization of symbols by approximation of their contour by curves; methods for optimal compression of scanned documents, algorithm for stitching parts of large originals; copy-protection methods by microprinting and embedding of hidden information to hardcopy; algorithmic approach for toner saving. In addition, method for integral printing is considered. Described techniques operate in automatic mode thanks to machine learning or ingenious heuristics. Most the techniques presented have a low computational complexity and memory consumption due to they were designed for firmware of embedded systems or software drivers. The book reflects the authors' practical experience in algorithm development for industrial R&D.

Since time immemorial, vision in general and images in particular have played an important and essential role in human life. Nowadays, the field of image processing also has numerous scientific, commercial, industrial and military applications. All these applications result from the interaction between fun damental scientific research on the one hand, and the development of new and high-standard technology on the other hand. Regarding the scientific com ponent, quite recently the scientific community became familiar with "fuzzy techniques" in image processing, which make use of the framework of fuzzy sets and related theories. The theory of fuzzy sets was initiated in 1965 by Zadeh, and is one of the most developed models to treat imprecision and uncertainty. Instead of the classical approach that an object belongs or does not belong to a set, the concept of a fuzzy set allows a gradual transition from membership to nonmembership, providing partial degrees of member ship. Fuzzy techniques are often complementary to existing techniques and can contribute to the development of better and more robust methods, as has already been illustrated in numerous scientific branches. With this vol ume, we want to demonstrate that the introduction and application of fuzzy techniques can also be very successful in the area of image processing. This book contains high-quality contributions of over 30 field experts, covering a wide range of both theoretical and practical applications of fuzzy techniques in image processing.

This volume helps to fill the gap between data analytics, image processing, and soft computing practices. Soft computing methods are used to focus on data analytics and image processing to develop good intelligent systems. To this end, readers of this volume will find quality research that presents the current trends, advanced methods, and hybridized techniques relating to data analytics and intelligent systems. The book also features case studies related to medical diagnosis with the use of image processing and soft computing algorithms in particular models. Providing extensive coverage of biometric systems, soft computing, image processing, artificial intelligence, and data analytics, the chapter authors discuss the latest research issues, present solutions to research problems, and look at comparative analysis with earlier results. Topics include some of the most important challenges and discoveries in intelligent systems today, such as computer vision concepts and image identification, data analysis and computational paradigms, deep learning techniques, face and speaker recognition systems, and more.

a balanced blend of theoretical and practical information that enables readers to develop 3D worlds quickly and efficiently.

This book is a collection of the latest applications of methods from soft computing and machine learning in image processing. It explores different areas ranging from image segmentation to the object recognition using complex approaches, and includes the theory of the methodologies used to provide an overview of the application of these tools in image processing. The material has been compiled from a scientific perspective, and the book is primarily intended for undergraduate and postgraduate science, engineering, and computational mathematics students. It can also be used for courses on artificial intelligence, advanced image processing, and computational intelligence, and is a valuable resource for researchers in the evolutionary computation, artificial intelligence and image processing communities.

The goal of this book is to present the topic of discrete calculus to scientists and - gineers and to show how the theory can be applied to solving a wide variety of re- world problems. We feel that discrete calculus allows us to unify many approaches to data analysis and content extraction while being accessible enough to be widely applied in many ?elds and disciplines. This project initially began as a tutorial on discrete calculus and its applications, and we hope that this work can provide an introduction to discrete calculus and demonstrate its effectiveness in many problem domains. This book bene?ted enormously from the friends and colleagues who provided software, data, and their time in reading the manuscript. In particular, for software we would like to thank Sebastien Bougleux for help with total variation code for an arbitrary graph and Camille Couprie for powerwatershed code. For use of their data, we wish to thank Jason Bohland, James Fowler, David Gleich, and Robert S- ner. For comments on the manuscript we would like to thank Christopher Alvino, Mukund Balasubramanian, Jason Bohland, Sebastien Bougleux, Gaelle Desbordes, Mathieu Desbrun, Noha El-Zehiry, Gareth Funka-Lea, David Gleich, Oliver Hinds, Anil Hirani, Hiroshi Ishikawa, Robert Kotiuga, Elliot Saltzman, Dheeraj Singaraju, Ganesh Sundaramoorthi and Enzo Tonti. We thank Eric Schwartz for all his help and support over the years. We would like to thank Wayne Wheeler, Simon Rees, and Catherine Brett at Springer for their enthusiasm and for initially proposing this project.

Provides a comprehensive guide about how to use machine vision for Industry 4.0 applications like analysis of images for automated inspections, object detection, object tracking etc. Includes case studies of Robotics Internet of Things with its current and future applications in Healthcare, Agriculture, Transportation, etc. It highlights the inclusion of impaired people in industry, like intelligent assistant that helps deaf-mute people to transmit instructions and warnings in a manufacturing process. It examines the significant technological advancements in machine vision for industrial Internet of things and explores the commercial benefits using the real world applications from healthcare to transportation. Provides a conceptual framework of Machine vision for the various Industrial applications. Addresses scientific aspects for a wider audience such as senior and junior engineers, undergraduate and post-graduate students, researchers, and anyone else interested in the trends, development, and opportunities for the Machine Vision for Industry 4.0 applications.

The book provides future research directions in IoT and image processing based Energy, Industry, and Healthcare domain and explores the different applications of its associated technologies. However, the Internet of Things and image processing is a very big field with a lot of subfields, which are very important such as Smart Homes to improve our daily life, Smart Cities to improve the citizens' life, Smart Towns to recover the livability and traditions, Smart Earth to protect our world, and Industrial Internet of Things to create safer and easier jobs. This book considers very important research areas in Energy, Industry, and Healthcare domain with IoT and image processing applications.The aim of the book to highlights future directions of optimization methods in various engineering and science applications in various IoT and image processing applications. Emphasis is given to deep learning and similar models of neural network-based learning techniques employed in solving optimization problems of different engineering and science applications. The role of AI in mechatronics is also highlighted using suitable optimization methods. This book considers very important research areas in Energy, Industry, and Healthcare. It addresses major issues and challenges in Energy, Industry, and Healthcare and solutions proposed for IoT-enabled cellular/computer networks, routing/communication protocols, surveillances applications, secured data management, and positioning approaches. It focuses mainly on smart and context-aware implementations. Key sailing Features: The impact of the proposed book is to provide a major area of concern to develop a foundation for the implementation process of new image processing and IoT devices based on Energy, Industry, and Healthcare related technology. The researchers working on image processing and IoT devices can correlate their work with other requirements of advanced technology in Energy, Industry, and Healthcare domain. To make aware of the latest technology like AI and Machine learning in Energy, Industry, and Healthcare related technology. Useful for the researcher to explore new things like Security, cryptography, and privacy in Energy, Industry, and Healthcare related technology. People who want to start in Energy, Industry, and Healthcare related technology with image processing and IoT world.

Image 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.

Volume 3 of the second edition of the fully revised and updated Digital Signal and Image Processing using MATLAB, after first two volumes on the "Fundamentals" and "Advances and Applications: The Deterministic Case", focuses on the stochastic case. It will be of particular benefit to readers who already possess a good knowledge of MATLAB, a command of the fundamental elements of digital signal processing and who are familiar with both the fundamentals of continuous-spectrum spectral analysis and who have a certain mathematical knowledge concerning Hilbert spaces. This volume is focused on applications, but it also provides a good presentation of the principles. A number of elements closer in nature to statistics than to signal processing itself are widely discussed. This choice comes from a current tendency of signal processing to use techniques from this field. More than 200 programs and functions are provided in the MATLAB language, with useful comments and guidance, to enable numerical experiments to be carried out, thus allowing readers to develop a deeper understanding of both the theoretical and practical aspects of this subject.

This book equips readers to understand a complex range of healthcare products that are used to diagnose, monitor, and treat diseases or medical conditions affecting humans. The first part of the book presents medical technologies such as medical information retrieval, tissue engineering techniques, 3D medical imaging, nanotechnology innovations in medicine, medical wireless sensor networks, and knowledge mining techniques in medicine. The second half of the book focuses on healthcare technologies including prediction hospital readmission risk, modeling e-health framework, personal Web in healthcare, security issues for medical records, and personalized services in healthcare. The contributors are leading world researchers who share their innovations, making this handbook the definitive resource on these topics. Handbook of Medical and Healthcare Technologies is intended for a wide audience including academicians, designers, developers, researchers and advanced-level students. It is also valuable for business managers, entrepreneurs, and investors within the medical and healthcare industries.

This book presents the topic of underwater real-time 3-D acoustical imaging covering the theory, algorithms and system design. It summarizes recent advances in wideband and ultra-wideband underwater real-time 3-D acoustical imaging, which will be very useful for developing next-generation systems. Through simulation techniques, readers are able to quickly learn and develop practical underwater real-time 3-D acoustical imaging systems of their own.