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.

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 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.

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.

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.

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.

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.

This is the second volume of Morgan Kaufmann's "GPU Computing Gems," offering an all-new set of insights, ideas, and practical "hands-on" skills from researchers and developers worldwide. Each chapter gives you a window into the work being performed across a variety of application domains, and the opportunity to witness the impact of parallel GPU computing on the efficiency of scientific research.

"GPU Computing Gems: Jade Edition" showcases the latest research solutions with GPGPU and CUDA, including: Improving memory access patterns for cellular automata using CUDALarge-scale gas turbine simulations on GPU clustersIdentifying and mitigating credit risk using large-scale economic capital simulationsGPU-powered MATLAB acceleration with JacketBiologically-inspired machine visionAn efficient CUDA algorithm for the maximum network flow problem"30 more chapters" of innovative GPU computing ideas, written to be accessible to researchers from any industry

"GPU Computing Gems: Jade Edition" contains 100% new material covering a variety of application domains: algorithms and data structures, engineering, interactive physics for games, computational finance, and programming tools.
This second volume of "GPU Computing Gems "offers 100% new material of interest across industry, including finance, medicine, imaging, engineering, gaming, environmental science, green computing, and more Covers new tools and frameworks for productive GPU computing application development and offers immediate benefit to researchers developing improved programming environments for GPUsEven more hands-on, proven techniques demonstrating how general purpose GPU computing is changing scientific research Distills the best practices of the community of CUDA programmers; each chapter provides insights and ideas as well as 'hands on' skills applicable to a variety of fields

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.

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 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.

This book gives a concise introduction to both image and video processing, providing a balanced coverage between theory, applications and standards. It gives an introduction to both 2-D and 3-D signal processing theory, supported by an introduction to random processes and some essential results from information theory, providing the necessary foundation for a full understanding of the image and video processing concepts that follow. A significant new feature is the explanation of practical network coding methods for image and video transmission. There is also coverage of new approaches such as: super-resolution methods, non-local processing, and directional transforms.

This book also has on-line support that contains many short MATLAB programs that complement examples and exercises on multidimensional signal, image, and video processing. There are numerous short video clips showing applications in video processing and coding, plus a copy of the vidview video player for playing .yuv video files on a Windows PC and an illustration of the effect of packet loss on H.264/AVC coded bitstreams.

New to this edition:

New appendices on random processes, information theory

New coverage of image analysis - edge detection, linking, clustering, and segmentation

Expanded coverage on image sensing and perception, including color spaces.

Now summarizes the new MPEG coding standards: scalable video coding (SVC) and multiview video coding (MVC), in addition to coverage of H.264/AVC.

Updated video processing material including new example on scalable video coding and more material on object- and region-based video coding.

More on video coding for networks including practical network coding (PNC), highlighting the significant advantages of PNC for both video downloading and streaming.

New coverage of super-resolution methods for image and video.
Only R&D level tutorial that gives an integrated treatment of image and video processing - topics that are interconnected.
New chapters on introductory random processes, information theory, and image enhancement and analysis
Coverage and discussion of the latest standards in video coding: H.264/AVC and the new scalable video standard (SVC)

Multimedia systems and content-based image retrieval are very important areas of research in computer technology. Numerous research works are being done in these fields at present. These two areas are changing our life-styles because they together cover creation, maintenance, accessing and retrieval of video, audio, image, textual and graphic data. But still several important issues in these areas remain unresolved and further research works are needed to be done for better techniques and applications. Multimedia Systems and Content-Based Image Retrieval addresses these unresolved issues and highlights current research.

A common complaint heard in today's business office is, There is paper everywhere but I cannot find the document that I'm looking for It is estimated that 90 percent of all data and information currently being processed and distributed within offices and between organizations resides on paper. Finding the physical space to store this paper can be a key problem. To overcome this problem and others, there is a need for a dramatic new approach to information processing found in business. Such an approach is found in this unique and useful volume.

Essentially, image processing systems in business use today's computer technology to solve paper processing and storage problems. Their main means of processing is performed electronically, that is, documents are captured initially on an electronic medium and forwarded to other users in the same mode. In this manner, their essential means of communicating with users is in an electronic format versus a paper one. Also, image processing systems in business are much more flexible in meeting changing user needs, especially when the data is stored on optical disk.

..".the perfect companion to "Programming Massively Parallel Processors" by Hwu & Kirk." -Nicolas Pinto, Research Scientist at Harvard & MIT, NVIDIA Fellow 2009-2010

Graphics processing units (GPUs) can do much more than render graphics. Scientists and researchers increasingly look to GPUs to improve the efficiency and performance of computationally-intensive experiments across a range of disciplines.

"GPU Computing Gems: Emerald Edition" brings their techniques to you, showcasing GPU-based solutions including: Black hole simulations with CUDAGPU-accelerated computation and interactive display of molecular orbitalsTemporal data mining for neuroscienceGPU -based parallelization for fast circuit optimizationFast graph cuts for computer visionReal-time stereo on GPGPU using progressive multi-resolution adaptive windowsGPU image demosaicingTomographic image reconstruction from unordered lines with CUDAMedical image processing using GPU -accelerated ITK image filters"41 more chapters" of innovative GPU computing ideas, written to be accessible to researchers from any domain

"GPU Computing Gems: Emerald Edition" is the first volume in Morgan Kaufmann's Applications of GPU Computing Series, offering the latest insights and research in computer vision, electronic design automation, emerging data-intensive applications, life sciences, medical imaging, ray tracing and rendering, scientific simulation, signal and audio processing, statistical modeling, and video / image processing.
Covers the breadth of industry from scientific simulation and electronic design automation to audio / video processing, medical imaging, computer vision, and moreMany examples leverage NVIDIA's CUDA parallel computing architecture, the most widely-adopted massively parallel programming solutionOffers insights and ideas as well as practical "hands-on" skills you can immediately put to use

This work offers a practical, non-theoretical introduction to the principles, application and benefits of multi-sensor technology in smart weapon development, surveillance systems, and an increasing variety of non-military applications. It also provides an overview of MMW (Millimeter-Wave) and IR (infrared) sensor constraints, design principles, system alternatives and targeting applications.

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 development of new-generation micro-manufacturing technologies and systems has revolutionised the way products are designed and manufactured today with a s- nificant impact in a number of key industrial sectors. Micro-manufacturing techno- gies are often described as disruptive, enabling and interdisciplinary leading to the creation of whole new classes of products that were previously not feasible to ma- facture. While key processes for volume manufacture of micro-parts such as mach- ing and moulding are becoming mature technologies, micro-assembly remains a key challenge for the cost-effective manufacture of complex micro-products. The ability to manufacture customizable micro-products that can be delivered in variable volumes within relatively short timescales is very much dependent on the level of development of the micro-assembly processes, positioning, alignment and measurement techniques, gripping and feeding approaches and devices. Micro-assembly has developed rapidly over the last few years and all the pred- tions are that it will remain a critical technology for high-value products in a number of key sectors such as healthcare, communications, defence and aerospace. The key challenge is to match the significant technological developments with a new gene- tion of micro-products that will establish firmly micro-assembly as a mature manuf- turing process. th The book includes the set of papers presented at the 5 International Precision - sembly Seminar IPAS 2010 held in Chamonix, France from the 14th to the 17th February 2010.

This text presents techniques for describing image textures. Contrary to the usual practice of embedding the images to known modelling frameworks borrowed from statistical physics or other domains, this book deduces the Gibbs models from basic image features and tailors the modelling framework to the images. This approach results in more general Gibbs models than can be either Markovian or non-Markovian and possess arbitrary interaction structures and strengths. The book presents computationally feasible algorithms for parameter estimation and image simulation and demonstrates their abilities and limitations by numerous experimental results. The book avoids too abstract mathematical constructions and gives explicit image-based explanations of all the notions involved.

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.

"Computer and Information Sciences" is a unique and comprehensive review of advanced technology and research in the field of Information Technology. It provides an up to date snapshot of research in Europe and the Far East (Hong Kong, Japan and China) in the most active areas of information technology, including Computer Vision, Data Engineering, Web Engineering, Internet Technologies, Bio-Informatics and System Performance Evaluation Methodologies.

This book provides a general overview of multiple instance learning (MIL), defining the framework and covering the central paradigms. The authors discuss the most important algorithms for MIL such as classification, regression and clustering. With a focus on classification, a taxonomy is set and the most relevant proposals are specified. Efficient algorithms are developed to discover relevant information when working with uncertainty. Key representative applications are included. This book carries out a study of the key related fields of distance metrics and alternative hypothesis. Chapters examine new and developing aspects of MIL such as data reduction for multi-instance problems and imbalanced MIL data. Class imbalance for multi-instance problems is defined at the bag level, a type of representation that utilizes ambiguity due to the fact that bag labels are available, but the labels of the individual instances are not defined. Additionally, multiple instance multiple label learning is explored. This learning framework introduces flexibility and ambiguity in the object representation providing a natural formulation for representing complicated objects. Thus, an object is represented by a bag of instances and is allowed to have associated multiple class labels simultaneously. This book is suitable for developers and engineers working to apply MIL techniques to solve a variety of real-world problems. It is also useful for researchers or students seeking a thorough overview of MIL literature, methods, and tools.

This volume contains the proceedings from two closely related workshops: Computational Diffusion MRI (CDMRI 13) and Mathematical Methods from Brain Connectivity (MMBC 13), held under the auspices of the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, which took place in Nagoya, Japan, September 2013.

Inside, readers will find contributions ranging from mathematical foundations and novel methods for the validation of inferring large-scale connectivity from neuroimaging data to the statistical analysis of the data, accelerated methods for data acquisition, and the most recent developments on mathematical diffusion modeling.

This volume offers a valuable starting point for anyone interested in learning computational diffusion MRI and mathematical methods for brain connectivity as well as offers new perspectives and insights on current research challenges for those currently in the field. It will be of interest to researchers and practitioners in computer science, MR physics, and applied mathematics.

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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.

A principal aim of computer graphics is to generate images that look as real as photographs. Realistic computer graphics imagery has however proven to be quite challenging to produce, since the appearance of materials arises from complicated physical processes that are difficult to analytically model and simulate, and image-based modeling of real material samples is often impractical due to the high-dimensional space of appearance data that needs to be acquired.

This book presents a general framework based on the inherent coherency in the appearance data of materials to make image-based appearance modeling more tractable. We observe that this coherence manifests itself as low-dimensional structure in the appearance data, and by identifying this structure we can take advantage of it to simplify the major processes in the appearance modeling pipeline. This framework consists of two key components, namely the coherence structure and the accompanying reconstruction method to fully recover the low-dimensional appearance data from sparse measurements. Our investigation of appearance coherency has led to three major forms of low-dimensional coherence structure and three types of coherency-based reconstruction upon which our framework is built.

This coherence-based approach can be comprehensively applied to all the major elements of image-based appearance modeling, from data acquisition of real material samples to user-assisted modeling from a photograph, from synthesis of volumes to editing of material properties, and from efficient rendering algorithms to physical fabrication of objects. In this book we present several techniques built on this coherency framework to handle various appearance modeling tasks both for surface reflections and subsurface scattering, the two primary physical components that generate material appearance. We believe that coherency-based appearance modeling will make it easier and more feasible for practitioners to bring computer graphics imagery to life.

This book is aimed towards readers with an interest in computer graphics. In particular, researchers, practitioners and students will benefit from this book by learning about the underlying coherence in appearance structure and how it can be utilized to improve appearance modeling. The specific techniques presented in our manuscript can be of value to anyone who wishes to elevate the realism of their computer graphics imagery. For understanding this book, an elementary background in computer graphics is assumed, such as from an introductory college course or from practical experience with computer graphics.