Architectures for Computer Vision. From Algorithm to Chip with Verilog

It is currently quite easy for students or designers/engineers to find very general books on the various aspects of safety, reliability and dependability of computer system architectures, and partial treatments of the elements that comprise an effective system architecture. It is not so easy to find a single source reference for all these aspects of system design. However, the purpose of this book is to present, in a single volume, a full description of all the constraints (including legal contexts around performance, reliability norms, etc.) and examples of architectures from various fields of application, including: railways, aeronautics, space, automobile and industrial automation. The content of the book is drawn from the experience of numerous people who are deeply immersed in the design and delivery (from conception to test and validation), safety (analysis of safety: FMEA, HA, etc.) and evaluation of critical systems. The involvement of real world industrial applications is handled in such as a way as to avoid problems of confidentiality, and thus allows for the inclusion of new, useful information (photos, architecture plans/schematics, real examples).

Uses Verilog HDL to illustrate computer architecture and microprocessor design, allowing readers to readily simulate and adjust the operation of each design, and thus build industrially relevant skills Introduces the computer principles, computer design, and how to use Verilog HDL (Hardware Description Language) to implement the design Provides the skills for designing processor/arithmetic/cpu chips, including the unique application of Verilog HDL material for CPU (central processing unit) implementation Despite the many books on Verilog and computer architecture and microprocessor design, few, if any, use Verilog as a key tool in helping a student to understand these design techniques A companion website includes color figures, Verilog HDL codes, extra test benches not found in the book, and PDFs of the figures and simulation waveforms for instructors

A cookbook of algorithms for common image processing applications Thanks to advances in computer hardware and software, algorithms have been developed that support sophisticated image processing without requiring an extensive background in mathematics. This bestselling book has been fully updated with the newest of these, including 2D vision methods in content-based searches and the use of graphics cards as image processing computational aids. It’s an ideal reference for software engineers and developers, advanced programmers, graphics programmers, scientists, and other specialists who require highly specialized image processing. Algorithms now exist for a wide variety of sophisticated image processing applications required by software engineers and developers, advanced programmers, graphics programmers, scientists, and related specialists This bestselling book has been completely updated to include the latest algorithms, including 2D vision methods in content-based searches, details on modern classifier methods, and graphics cards used as image processing computational aids Saves hours of mathematical calculating by using distributed processing and GPU programming, and gives non-mathematicians the shortcuts needed to program relatively sophisticated applications. Algorithms for Image Processing and Computer Vision, 2nd Edition provides the tools to speed development of image processing applications.

Provides comprehensive coverage of theory and hands-on implementation of computer vision-based sensors for structural health monitoring This book is the first to fill the gap between scientific research of computer vision and its practical applications for structural health monitoring (SHM). It provides a complete, state-of-the-art review of the collective experience that the SHM community has gained in recent years. It also extensively explores the potentials of the vision sensor as a fast and cost-effective tool for solving SHM problems based on both time and frequency domain analytics, broadening the application of emerging computer vision sensor technology in not only scientific research but also engineering practice. Computer Vision for Structural Dynamics and Health Monitoring presents fundamental knowledge, important issues, and practical techniques critical to successful development of vision-based sensors in detail, including robustness of template matching techniques for tracking targets; coordinate conversion methods for determining calibration factors to convert image pixel displacements to physical displacements; sensing by tracking artificial targets vs. natural targets; measurements in real time vs. by post-processing; and field measurement error sources and mitigation methods. The book also features a wide range of tests conducted in both controlled laboratory and complex field environments in order to evaluate the sensor accuracy and demonstrate the unique features and merits of computer vision-based structural displacement measurement. Offers comprehensive understanding of the principles and applications of computer vision for structural dynamics and health monitoring Helps broaden the application of the emerging computer vision sensor technology from scientific research to engineering practice such as field condition assessment of civil engineering structures and infrastructure systems Includes a wide range of laboratory and field testing examples, as well as practical techniques for field application Provides MATLAB code for most of the issues discussed including that of image processing, structural dynamics, and SHM applications Computer Vision for Structural Dynamics and Health Monitoring is ideal for graduate students, researchers, and practicing engineers who are interested in learning about this emerging sensor technology and advancing their applications in SHM and other engineering problems. It will also benefit those in civil and aerospace engineering, energy, and computer science.

Master digital design with VLSI and Verilog using this up-to-date and comprehensive resource from leaders in the field Digital VLSI Design Problems and Solution with Verilog delivers an expertly crafted treatment of the fundamental concepts of digital design and digital design verification with Verilog HDL. The book includes the foundational knowledge that is crucial for beginners to grasp, along with more advanced coverage suitable for research students working in the area of VLSI design. Including digital design information from the switch level to FPGA-based implementation using hardware description language (HDL), the distinguished authors have created a one-stop resource for anyone in the field of VLSI design. Through eleven insightful chapters, you'll learn the concepts behind digital circuit design, including combinational and sequential circuit design fundamentals based on Boolean algebra. You'll also discover comprehensive treatments of topics like logic functionality of complex digital circuits with Verilog, using software simulators like ISim of Xilinx. The distinguished authors have included additional topics as well, like: A discussion of programming techniques in Verilog, including gate level modeling, model instantiation, dataflow modeling, and behavioral modeling A treatment of programmable and reconfigurable devices, including logic synthesis, introduction of PLDs, and the basics of FPGA architecture An introduction to System Verilog, including its distinct features and a comparison of Verilog with System Verilog A project based on Verilog HDLs, with real-time examples implemented using Verilog code on an FPGA board Perfect for undergraduate and graduate students in electronics engineering and computer science engineering, Digital VLSI Design Problems and Solution with Verilog also has a place on the bookshelves of academic researchers and private industry professionals in these fields.

Computer vision encompasses the construction of integrated vision systems and the application of vision to problems of real-world importance. The process of creating 3D models is still rather difficult, requiring mechanical measurement of the camera positions or manual alignment of partial 3D views of a scene. However using algorithms, it is possible to take a collection of stereo-pair images of a scene and then automatically produce a photo-realistic, geometrically accurate digital 3D model. This book provides a comprehensive introduction to the methods, theories and algorithms of 3D computer vision. Almost every theoretical issue is underpinned with practical implementation or a working algorithm using pseudo-code and complete code written in C++ and MatLab®. There is the additional clarification of an accompanying website with downloadable software, case studies and exercises. Organised in three parts, Cyganek and Siebert give a brief history of vision research, and subsequently: present basic low-level image processing operations for image matching, including a separate chapter on image matching algorithms; explain scale-space vision, as well as space reconstruction and multiview integration; demonstrate a variety of practical applications for 3D surface imaging and analysis; provide concise appendices on topics such as the basics of projective geometry and tensor calculus for image processing, distortion and noise in images plus image warping procedures. An Introduction to 3D Computer Vision Algorithms and Techniques is a valuable reference for practitioners and programmers working in 3D computer vision, image processing and analysis as well as computer visualisation. It would also be of interest to advanced students and researchers in the fields of engineering, computer science, clinical photography, robotics, graphics and mathematics.

The next generation of computer system designers will be less concerned about details of processors and memories, and more concerned about the elements of a system tailored to particular applications. These designers will have a fundamental knowledge of processors and other elements in the system, but the success of their design will depend on the skills in making system-level tradeoffs that optimize the cost, performance and other attributes to meet application requirements. This book provides a new treatment of computer system design, particularly for System-on-Chip (SOC), which addresses the issues mentioned above. It begins with a global introduction, from the high-level view to the lowest common denominator (the chip itself), then moves on to the three main building blocks of an SOC (processor, memory, and interconnect). Next is an overview of what makes SOC unique (its customization ability and the applications that drive it). The final chapter presents future challenges for system design and SOC possibilities.

While the field of computer vision drives many of today’s digital technologies and communication networks, the topic of color has emerged only recently in most computer vision applications. One of the most extensive works to date on color in computer vision, this book provides a complete set of tools for working with color in the field of image understanding. Based on the authors’ intense collaboration for more than a decade and drawing on the latest thinking in the field of computer science, the book integrates topics from color science and computer vision, clearly linking theories, techniques, machine learning, and applications. The fundamental basics, sample applications, and downloadable versions of the software and data sets are also included. Clear, thorough, and practical, Color in Computer Vision explains: Computer vision, including color-driven algorithms and quantitative results of various state-of-the-art methods Color science topics such as color systems, color reflection mechanisms, color invariance, and color constancy Digital image processing, including edge detection, feature extraction, image segmentation, and image transformations Signal processing techniques for the development of both image processing and machine learning Robotics and artificial intelligence, including such topics as supervised learning and classifiers for object and scene categorization Researchers and professionals in computer science, computer vision, color science, electrical engineering, and signal processing will learn how to implement color in computer vision applications and gain insight into future developments in this dynamic and expanding field.

A computational perspective on partial order and lattice theory, focusing on algorithms and their applications This book provides a uniform treatment of the theory and applications of lattice theory. The applications covered include tracking dependency in distributed systems, combinatorics, detecting global predicates in distributed systems, set families, and integer partitions. The book presents algorithmic proofs of theorems whenever possible. These proofs are written in the calculational style advocated by Dijkstra, with arguments explicitly spelled out step by step. The author’s intent is for readers to learn not only the proofs, but the heuristics that guide said proofs. Introduction to Lattice Theory with Computer Science Applications: Examines; posets, Dilworth’s theorem, merging algorithms, lattices, lattice completion, morphisms, modular and distributive lattices, slicing, interval orders, tractable posets, lattice enumeration algorithms, and dimension theory Provides end of chapter exercises to help readers retain newfound knowledge on each subject Includes supplementary material at www.ece.utexas.edu/~garg Introduction to Lattice Theory with Computer Science Applications is written for students of computer science, as well as practicing mathematicians.

The aim of this book is to motivate students into learning Machine Analysis by reinforcing theory and applications throughout the text. The author uses an enthusiastic ‘hands-on’ approach by including photos of actual mechanisms in place of abstract line illustrations, and directs students towards developing their own software for mechanism analysis using Excel & Matlab. An accompanying website includes a detailed list of tips for learning machine analysis, including tips on working homework problems, note taking, preparing for tests, computer programming and other topics to aid in student success. Study guides for each chapter that focus on teaching the thought process needed to solve problems by presenting practice problems are included, as are computer animations for common mechanisms discussed in the text.