The integration of the 3rd dimension in the production of spatial representation is largely recognized as a valuable approach to comprehend our reality, that is 3D. During the last decade developments in 3D Geoinformation (GI) system have made substantial progress. We are about to have a more complete spatial model and understanding of our planet in different scales. Hence, various communities and cities offer 3D landscape and 3D city models as valuable source and instrument for sustainable management of rural and urban resources. Also municipal utilities, real estate companies benefit from recent developments related to 3D applications. In order to present recent developments and to discuss future trends, academics and practitioners met at the 7th International Workshop on 3D Geoinformation. This book comprises a selection of evaluated, high quality papers that were presented at this workshop in May 2012. The topics focus explicitly on the last achievements (methods, algorithms, models, systems) with respect to 3D GeoInformation requirements. The book is aimed at decision makers and experts as well at students interested in the 3D component of geographical information science including GI engineers, computer scientists, photogrammetrists, land surveyors, urban planners, and mapping specialists.

In recent years, the paradigm of video coding has shifted from that of a frame-based approach to a content-based approach, particularly with the finalization of the ISO multimedia coding standard, MPEG-4. MPEG-4 is the emerging standard for the coding of multimedia content. It defines a syntax for a set of content-based functionalities, namely, content-based interactivity, compression and universal access. However, it does not specify how the video content is to be generated. To generate the video content, video has to be segmented into video objects and tracked as they transverse across the video frames. This book addresses the difficult problem of video segmentation, and the extraction and tracking of video object planes as defined in MPEG-4. It then focuses on the specific issue of face segmentation and coding as applied to videoconferencing in order to improve the quality of videoconferencing images especially in the facial region.
Modal-based coding is a content-based coding technique used to code synthetic objects that have become an important part of video content. It results in extremely low bit rates because only the parameters needed to represent the modal are transmitted. Model-based coding is included to provide background information for the synthetic object coding in MPEG-4. Lastly, MPEG-4, the first coding standard for multimedia content is described in detail. The topics covered include the coding of audio objects, the coding of natural and synthetic video objects, and error resilience.
"Advanced Video Coding" is one of the first books on content-based coding and MPEG-4 coding standard. It serves as an excellent information source and reference for both researchers and practicing engineers.

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 examines paintings using a computational and quantitative approach. Specifically, it compares paintings to photographs, addressing the strengths and limitations of both. Particular aesthetic practices are examined such as the vista, foreground to background organisation and the depth planes. These are analysed using a range of computational approaches and clear observations are made. New generations of image-capture devices such as Google goggles and the light field camera, promise a future in which the formal attributes of a photograph are made available for editing to a degree that has hitherto been the exclusive territory of painting. In this sense paintings and photographs are converging, and it therefore seems an opportune time to study the comparisons between them. In this context, the book includes cutting-edge work examining how some of the aesthetic attributes of a painting can be transferred to a photograph using the latest computational approaches.

Praise for Previous Volumes
"This book will be a useful reference to control engineers and researchers. The papers contained cover well the recent advances in the field of modern control theory."
-IEEE GROUP CORRESPONDENCE
"This book will help all those researchers who valiantly try to keep abreast of what is new in the theory and practice of optimal control."
-CONTROL

The book describes recent research results in the areas of modelling, creation, management and presentation of interactive 3D multimedia content. The book describes the current state of the art in the field and identifies the most important research and design issues. Consecutive chapters address these issues. These are: database modelling of 3D content, security in 3D environments, describing interactivity of content, searching content, visualization of search results, modelling mixed reality content, and efficient creation of interactive 3D content. Each chapter is illustrated with example applications based on the proposed approach. The final chapter discusses some important ethical issues related to the widespread use of virtual environments in everyday life. The book provides ready to use solutions for many important problems related to the creation of interactive 3D multimedia applications and will be a primary reading for researchers and developers working in this domain.

Vision-based control of wheeled mobile robots is an interesting field of research from a scientific and even social point of view due to its potential applicability. This book presents a formal treatment of some aspects of control theory applied to the problem of vision-based pose regulation of wheeled mobile robots. In this problem, the robot has to reach a desired position and orientation, which are specified by a target image. It is faced in such a way that vision and control are unified to achieve stability of the closed loop, a large region of convergence, without local minima and good robustness against parametric uncertainty. Three different control schemes that rely on monocular vision as unique sensor are presented and evaluated experimentally. A common benefit of these approaches is that they are valid for imaging systems obeying approximately a central projection model, e.g., conventional cameras, catadioptric systems and some fisheye cameras. Thus, the presented control schemes are generic approaches. A minimum set of visual measurements, integrated in adequate task functions, are taken from a geometric constraint imposed between corresponding image features. Particularly, the epipolar geometry and the trifocal tensor are exploited since they can be used for generic scenes. A detailed experimental evaluation is presented for each control scheme.

Color perception plays an important role in object recognition and scene understanding both for humans and intelligent vision systems. Recent advances in digital color imaging and computer hardware technology have led to an explosion in the use of color images in a variety of applications including medical imaging, content-based image retrieval, biometrics, watermarking, digital inpainting, remote sensing, visual quality inspection, among many others. As a result, automated processing and analysis of color images has become an active area of research, to which the large number of publications of the past two decades bears witness. The multivariate nature of color image data presents new challenges for researchers and practitioners as the numerous methods developed for single channel images are often not directly applicable to multichannel ones. The goal of this volume is to summarize the state-of-the-art in the early stages of the color image processing pipeline."

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.

A 3D user interface (3DUI) is an interface in which the user performs tasks in three dimensions. For example, interactions using hand/body gestures, interaction using a motion controller (e.g. Sony PlayStation Move), interaction with virtual reality devices using tracked motion controllers, etc. All these technologies which let a user interact in three dimensions are called 3D user interface technologies. These 3D user interfaces have the potential to make games more immersive & engaging and thus potentially provide a better user experience to gamers. Although 3D user interface technologies are available for games, it is unclear how their usage affects game play and if there are any user performance benefits. This book presents state of the art research on exploring 3D user interface technologies for improving video games. It also presents a review of research work done in this area and describes experiments focused on usage of stereoscopic 3D, head tracking, and hand gesture-based control in gaming scenarios. These experiments are systematic studies in gaming environments and are aimed at understanding the effect of the underlined 3D interface technology on the gaming experience of a user. Based on these experiments, several design guidelines are presented which can aid game designers in designing better immersive games.

The need of video compression in the modern age of visual communication cannot be over-emphasized. This monograph will provide useful information to the postgraduate students and researchers who wish to work in the domain of VLSI design for video processing applications. In this book, one can find an in-depth discussion of several motion estimation algorithms and their VLSI implementation as conceived and developed by the authors. It records an account of research done involving fast three step search, successive elimination, one-bit transformation and its effective combination with diamond search and dynamic pixel truncation techniques. Two appendices provide a number of instances of proof of concept through Matlab and Verilog program segments. In this aspect, the book can be considered as first of its kind. The architectures have been developed with an eye to their applicability in everyday low-power handheld appliances including video camcorders and smartphones.

For those involved with the design and analysis of electro-optical systems, the book outlines current and future ground, air and spacebourne applications of electro-optical systems. It describes their performance requirements and practical methods of achieving design objectives.

This book lies at the interface of machine learning - a subfield of computer science that develops algorithms for challenging tasks such as shape or image recognition, where traditional algorithms fail - and photonics - the physical science of light, which underlies many of the optical communications technologies used in our information society. It provides a thorough introduction to reservoir computing and field-programmable gate arrays (FPGAs). Recently, photonic implementations of reservoir computing (a machine learning algorithm based on artificial neural networks) have made a breakthrough in optical computing possible. In this book, the author pushes the performance of these systems significantly beyond what was achieved before. By interfacing a photonic reservoir computer with a high-speed electronic device (an FPGA), the author successfully interacts with the reservoir computer in real time, allowing him to considerably expand its capabilities and range of possible applications. Furthermore, the author draws on his expertise in machine learning and FPGA programming to make progress on a very different problem, namely the real-time image analysis of optical coherence tomography for atherosclerotic arteries.

Continuing in the footsteps of the pioneering first edition, Signal and Image Processing for Remote Sensing, Second Edition explores the most up-to-date signal and image processing methods for dealing with remote sensing problems. Although most data from satellites are in image form, signal processing can contribute significantly in extracting information from remotely sensed waveforms or time series data. This book combines both, providing a unique balance between the role of signal processing and image processing.

Featuring contributions from worldwide experts, this book continues to emphasize mathematical approaches. Not limited to satellite data, it also considers signals and images from hydroacoustic, seismic, microwave, and other sensors. Chapters cover important topics in signal and image processing and discuss techniques for dealing with remote sensing problems. Each chapter offers an introduction to the topic before delving into research results, making the book accessible to a broad audience.

This second edition reflects the considerable advances that have occurred in the field, with 23 of 27 chapters being new or entirely rewritten. Coverage includes new mathematical developments such as compressive sensing, empirical mode decomposition, and sparse representation, as well as new component analysis methods such as non-negative matrix and tensor factorization. The book also presents new experimental results on SAR and hyperspectral image processing.

The emphasis is on mathematical techniques that will far outlast the rapidly changing sensor, software, and hardware technologies. Written for industrial and academic researchers and graduate students alike, this book helps readers connect the "dots" in image and signal processing.

New in This Edition

The second edition includes four chapters from the first edition, plus 23 new or entirely rewritten chapters, and 190 new figures. New topics covered include:

• Compressive sensing
• The mixed pixel problem with hyperspectral images
• Hyperspectral image (HSI) target detection and classification based on sparse representation
• An ISAR technique for refocusing moving targets in SAR images
• Empirical mode decomposition for signal processing
• Feature extraction for classification of remote sensing signals and images
• Active learning methods in classification of remote sensing images
• Signal subspace identification of hyperspectral data
• Wavelet-based multi/hyperspectral image restoration and fusion

The second edition is not intended to replace the first edition entirely and readers are encouraged to read both editions of the book for a more complete picture of signal and image processing in remote sensing. See Signal and Image Processing for Remote Sensing (CRC Press 2006).

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.

Since the early 20th century, medical imaging has been dominated by monochrome imaging modalities such as x-ray, computed tomography, ultrasound, and magnetic resonance imaging. As a result, color information has been overlooked in medical image analysis applications. Recently, various medical imaging modalities that involve color information have been introduced. These include cervicography, dermoscopy, fundus photography, gastrointestinal endoscopy, microscopy, and wound photography. However, in comparison to monochrome images, the analysis of color images is a relatively unexplored area. The multivariate nature of color image data presents new challenges for researchers and practitioners as the numerous methods developed for monochrome images are often not directly applicable to multichannel images. The goal of this volume is to summarize the state-of-the-art in the utilization of color information in medical image analysis.

This book describes the development of a new low-cost medium wavelength IR (MWIR) monolithic imager technology for high-speed uncooled industrial applications. It takes the baton on the latest technological advances in the field of vapor phase deposition (VPD) PbSe-based MWIR detection accomplished by the industrial partner NIT S.L., adding fundamental knowledge on the investigation of novel VLSI analog and mixed-signal design techniques at circuit and system levels for the development of the readout integrated device attached to the detector. In order to fulfill the operational requirements of VPD PbSe, this work proposes null inter-pixel crosstalk vision sensor architectures based on a digital-only focal plane array (FPA) of configurable pixel sensors. Each digital pixel sensor (DPS) cell is equipped with fast communication modules, self-biasing, offset cancellation, analog-to-digital converter (ADC) and fixed pattern noise (FPN) correction. In-pixel power consumption is minimized by the use of comprehensive MOSFET subthreshold operation.

The book puts special stress on the contemporary techniques for reasoning-based image processing and analysis: learning based image representation and advanced video coding; intelligent image processing and analysis in medical vision systems; similarity learning models for image reconstruction; visual perception for mobile robot motion control, simulation of human brain activity in the analysis of video sequences; shape-based invariant features extraction; essential of paraconsistent neural networks, creativity and intelligent representation in computational systems. The book comprises 14 chapters. Each chapter is a small monograph, representing resent investigations of authors in the area. The topics of the chapters cover wide scientific and application areas and complement each-other very well. The chapters' content is based on fundamental theoretical presentations, followed by experimental results and comparison with similar techniques. The size of the chapters is well-ballanced which permits a thorough presentation of the investigated problems. The authors are from universities and R&D institutions all over the world; some of the chapters are prepared by international teams. The book will be of use for university and PhD students, researchers and software developers working in the area of digital image and video processing and analysis.

Praise for Previous Volumes
"This book will be a useful reference to control engineers and researchers. The papers contained cover well the recent advances in the field of modern control theory."
-IEEE CONTROL CORRESPONDANCE
" This book will help all those researchers wjo valiantly try to keep abreast of what is new in the theory and practice of optimal control."
-CONTROL

This thesis demonstrates techniques that provide faster and more accurate solutions to a variety of problems in machine learning and signal processing. The author proposes a "greedy" algorithm, deriving sparse solutions with guarantees of optimality. The use of this algorithm removes many of the inaccuracies that occurred with the use of previous models.

Modern-day radar problems can't be solved by conventional radar technology -- but they can be solved with the ground-breaking signal processing and analysis methods presented in this unique, expertly authored book. Illustrated with nearly 400 figures and including over 70 equations.

Image Processing and Pattern Recognition covers major applications in the field, including optical character recognition, speech classification, medical imaging, paper currency recognition, classification reliability techniques, and sensor technology. The text emphasizes algorithms and architectures for achieving practical and effective systems, and presents many examples. Practitioners, researchers, and students in computer science, electrical engineering, andradiology, as well as those working at financial institutions, will value this unique and authoritative reference to diverse applications methodologies.
Key Features
* Coverage includes:
* Optical character recognition
* Speech classification
* Medical imaging
* Paper currency recognition
* Classification reliability techniques
* Sensor technology
Algorithms and architectures for achieving practical and effective systems are emphasized, with many examples illustrating the text. Practitioners, researchers, and students in computer science, electrical engineering, and radiology, as wellk as those working at financial institutions, will find this volume a unique and comprehensive reference source for this diverse applications area.

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.