Author: Branislav Kisacanin
Publisher: Springer Science & Business Media
ISBN: 1848003048
Category : Computers
Languages : en
Pages : 300
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Book Description
As a graduate student at Ohio State in the mid-1970s, I inherited a unique c- puter vision laboratory from the doctoral research of previous students. They had designed and built an early frame-grabber to deliver digitized color video from a (very large) electronic video camera on a tripod to a mini-computer (sic) with a (huge!) disk drive—about the size of four washing machines. They had also - signed a binary image array processor and programming language, complete with a user’s guide, to facilitate designing software for this one-of-a-kindprocessor. The overall system enabled programmable real-time image processing at video rate for many operations. I had the whole lab to myself. I designed software that detected an object in the eldofview,trackeditsmovementsinrealtime,anddisplayedarunningdescription of the events in English. For example: “An object has appeared in the upper right corner...Itismovingdownandtotheleft...Nowtheobjectisgettingcloser...The object moved out of sight to the left”—about like that. The algorithms were simple, relying on a suf cient image intensity difference to separate the object from the background (a plain wall). From computer vision papers I had read, I knew that vision in general imaging conditions is much more sophisticated. But it worked, it was great fun, and I was hooked.
Author: S. R. Vijayalakshmi
Publisher:
ISBN: 9781683924579
Category : Computers
Languages : en
Pages : 0
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Book Description
An introductory guide for anyone who is interested in designing machines that have vision-enabled, embedded products, this book covers topics encountered in hardware architecture, software algorithms, applications, advancements in processors and sensors. --
Author: Scott Krig
Publisher: Apress
ISBN: 1430259302
Category : Computers
Languages : en
Pages : 498
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Book Description
Computer Vision Metrics provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features. This book provides necessary background to develop intuition about why interest point detectors and feature descriptors actually work, how they are designed, with observations about tuning the methods for achieving robustness and invariance targets for specific applications. The survey is broader than it is deep, with over 540 references provided to dig deeper. The taxonomy includes search methods, spectra components, descriptor representation, shape, distance functions, accuracy, efficiency, robustness and invariance attributes, and more. Rather than providing ‘how-to’ source code examples and shortcuts, this book provides a counterpoint discussion to the many fine opencv community source code resources available for hands-on practitioners.
Author: Samarth Brahmbhatt
Publisher: Apress
ISBN: 1430260793
Category : Computers
Languages : en
Pages : 229
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Book Description
Practical OpenCV is a hands-on project book that shows you how to get the best results from OpenCV, the open-source computer vision library. Computer vision is key to technologies like object recognition, shape detection, and depth estimation. OpenCV is an open-source library with over 2500 algorithms that you can use to do all of these, as well as track moving objects, extract 3D models, and overlay augmented reality. It's used by major companies like Google (in its autonomous car), Intel, and Sony; and it is the backbone of the Robot Operating System’s computer vision capability. In short, if you're working with computer vision at all, you need to know OpenCV. With Practical OpenCV, you'll be able to: Get OpenCV up and running on Windows or Linux. Use OpenCV to control the camera board and run vision algorithms on Raspberry Pi. Understand what goes on behind the scenes in computer vision applications like object detection, image stitching, filtering, stereo vision, and more. Code complex computer vision projects for your class/hobby/robot/job, many of which can execute in real time on off-the-shelf processors. Combine different modules that you develop to create your own interactive computer vision app. What you’ll learn The ins and outs of OpenCV programming on Windows and Linux Transforming and filtering images Detecting corners, edges, lines, and circles in images and video Detecting pre-trained objects in images and video Making panoramas by stitching images together Getting depth information by using stereo cameras Basic machine learning techniques BONUS: Learn how to run OpenCV on Raspberry Pi Who this book is for This book is for programmers and makers with little or no previous exposure to computer vision. Some proficiency with C++ is required. Table of ContentsPart 1: Getting comfortable Chapter 1: Introduction to Computer Vision and OpenCV Chapter 2: Setting up OpenCV on your computer Chapter 3: CV Bling – OpenCV inbuilt demos Chapter 4: Basic operations on images and GUI windows Part 2: Advanced computer vision problems and coding them in OpenCV Chapter 5: Image filtering Chapter 6: Shapes in images Chapter 7: Image segmentation and histograms Chapter 8: Basic machine learning and keypoint-based object detection Chapter 9: Affine and Perspective transformations and their applications to image panoramas Chapter 10: 3D geometry and stereo vision Chapter 11: Embedded computer vision: Running OpenCV programs on the Raspberry Pi
Author: Alireza Moini
Publisher: Springer Science & Business Media
ISBN: 1461552672
Category : Computers
Languages : en
Pages : 305
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Book Description
This chapter presents a set of introductory material, which in addition to providing a general view on the topic, highlights the importance of research in this area. It also presents a short history of the design of smart vision sensors, and points out some of the fundamental issues in the design of such sensors. 1. 1 A General Overview Machine vision is one of the main branches of artificial intelligence. The richness of information present in images makes them the first choice as an input to an artificial system which tries to interact with its environment. A large proportion of the brain of many advanced species is dedicated to visual information processing, which illustrates the importance of visual information in biological systems. Biological visual systems have evolved over millions of years, and each specie has developed a specialized visual system tailored for the essential tasks of survival, such as catching a prey, or escaping a predator. Implementing electronic hardware for image processing, therefore, may benefit from the underlying fundamental aspects of biological vision, though in no respect should this be regarded as a solid framework for electronic vision systems. Traditionally, computer vision algorithms are performed on images captured by conventional cameras, and processing is accomplished by means of general purpose digital computers. More advanced systems utilize dedicated hardware to speed up the processing stage.
Author: Donald G. Bailey
Publisher: John Wiley & Sons
ISBN: 0470828528
Category : Technology & Engineering
Languages : en
Pages : 503
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Book Description
Dr Donald Bailey starts with introductory material considering the problem of embedded image processing, and how some of the issues may be solved using parallel hardware solutions. Field programmable gate arrays (FPGAs) are introduced as a technology that provides flexible, fine-grained hardware that can readily exploit parallelism within many image processing algorithms. A brief review of FPGA programming languages provides the link between a software mindset normally associated with image processing algorithms, and the hardware mindset required for efficient utilization of a parallel hardware design. The design process for implementing an image processing algorithm on an FPGA is compared with that for a conventional software implementation, with the key differences highlighted. Particular attention is given to the techniques for mapping an algorithm onto an FPGA implementation, considering timing, memory bandwidth and resource constraints, and efficient hardware computational techniques. Extensive coverage is given of a range of low and intermediate level image processing operations, discussing efficient implementations and how these may vary according to the application. The techniques are illustrated with several example applications or case studies from projects or applications he has been involved with. Issues such as interfacing between the FPGA and peripheral devices are covered briefly, as is designing the system in such a way that it can be more readily debugged and tuned. Provides a bridge between algorithms and hardware Demonstrates how to avoid many of the potential pitfalls Offers practical recommendations and solutions Illustrates several real-world applications and case studies Allows those with software backgrounds to understand efficient hardware implementation Design for Embedded Image Processing on FPGAs is ideal for researchers and engineers in the vision or image processing industry, who are looking at smart sensors, machine vision, and robotic vision, as well as FPGA developers and application engineers. The book can also be used by graduate students studying imaging systems, computer engineering, digital design, circuit design, or computer science. It can also be used as supplementary text for courses in advanced digital design, algorithm and hardware implementation, and digital signal processing and applications. Companion website for the book: www.wiley.com/go/bailey/fpga
Author: Scott Krig
Publisher: Springer
ISBN: 3319337629
Category : Computers
Languages : en
Pages : 653
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Book Description
Based on the successful 2014 book published by Apress, this textbook edition is expanded to provide a comprehensive history and state-of-the-art survey for fundamental computer vision methods and deep learning. With over 800 essential references, as well as chapter-by-chapter learning assignments, both students and researchers can dig deeper into core computer vision topics and deep learning architectures. The survey covers everything from feature descriptors, regional and global feature metrics, feature learning architectures, deep learning, neuroscience of vision, neural networks, and detailed example architectures to illustrate computer vision hardware and software optimization methods. To complement the survey, the textbook includes useful analyses which provide insight into the goals of various methods, why they work, and how they may be optimized. The text delivers an essential survey and a valuable taxonomy, thus providing a key learning tool for students, researchers and engineers, to supplement the many effective hands-on resources and open source projects, such as OpenCV and other imaging and deep learning tools.
Author: Lewin Edwards
Publisher: Elsevier
ISBN: 0080491235
Category : Technology & Engineering
Languages : en
Pages : 249
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Book Description
In this practical guide, experienced embedded engineer Lewin Edwards demonstrates faster, lower-cost methods for developing high-end embedded systems. With today's tight schedules and lower budgets, embedded designers are under greater pressure to deliver prototypes and system designs faster and cheaper. Edwards demonstrates how the use of the right tools and operating systems can make seemingly impossible deadlines possible. Designer's Guide to Embedded Systems Development shares many advanced, in-the-trenches design secrets to help engineers achieve better performance on the job. In particular, it covers many of the newer design tools supported by the GPL (GNU Public License) system. Code examples are given to provide concrete illustrations of tasks described in the text. The general procedures are applicable to many possible projects based on any 16/32-bit microcontroller. The book covers choosing the right architecture and development hardware to fit the project; choosing an operating system and developing a toolchain; evaluating software licenses and how they affect a project; step-by-step building instructions for gcc, binutils, gdb and newlib for the ARM7 core used in the case study project; prototyping techniques using a custom printed circuit board; debugging tips; and portability considerations. A wealth of practical tips, tricks and techniques Design better, faster and more cost-effectively
Author: Thomas Bräunl
Publisher: Springer Science & Business Media
ISBN: 3540705341
Category : Computers
Languages : en
Pages : 536
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Book Description
This book presents a unique examination of mobile robots and embedded systems, from introductory to intermediate level. It is structured in three parts, dealing with Embedded Systems (hardware and software design, actuators, sensors, PID control, multitasking), Mobile Robot Design (driving, balancing, walking, and flying robots), and Mobile Robot Applications (mapping, robot soccer, genetic algorithms, neural networks, behavior-based systems, and simulation). The book is written as a text for courses in computer science, computer engineering, IT, electronic engineering, and mechatronics, as well as a guide for robot hobbyists and researchers.
Author: Bhaumik Vaidya
Publisher: Packt Publishing Ltd
ISBN: 1789343682
Category : Computers
Languages : en
Pages : 373
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Book Description
Discover how CUDA allows OpenCV to handle complex and rapidly growing image data processing in computer and machine vision by accessing the power of GPU Key FeaturesExplore examples to leverage the GPU processing power with OpenCV and CUDAEnhance the performance of algorithms on embedded hardware platformsDiscover C++ and Python libraries for GPU accelerationBook Description Computer vision has been revolutionizing a wide range of industries, and OpenCV is the most widely chosen tool for computer vision with its ability to work in multiple programming languages. Nowadays, in computer vision, there is a need to process large images in real time, which is difficult to handle for OpenCV on its own. This is where CUDA comes into the picture, allowing OpenCV to leverage powerful NVDIA GPUs. This book provides a detailed overview of integrating OpenCV with CUDA for practical applications. To start with, you’ll understand GPU programming with CUDA, an essential aspect for computer vision developers who have never worked with GPUs. You’ll then move on to exploring OpenCV acceleration with GPUs and CUDA by walking through some practical examples. Once you have got to grips with the core concepts, you’ll familiarize yourself with deploying OpenCV applications on NVIDIA Jetson TX1, which is popular for computer vision and deep learning applications. The last chapters of the book explain PyCUDA, a Python library that leverages the power of CUDA and GPUs for accelerations and can be used by computer vision developers who use OpenCV with Python. By the end of this book, you’ll have enhanced computer vision applications with the help of this book's hands-on approach. What you will learnUnderstand how to access GPU device properties and capabilities from CUDA programsLearn how to accelerate searching and sorting algorithmsDetect shapes such as lines and circles in imagesExplore object tracking and detection with algorithmsProcess videos using different video analysis techniques in Jetson TX1Access GPU device properties from the PyCUDA programUnderstand how kernel execution worksWho this book is for This book is a go-to guide for you if you are a developer working with OpenCV and want to learn how to process more complex image data by exploiting GPU processing. A thorough understanding of computer vision concepts and programming languages such as C++ or Python is expected.
