Imaging and Vision Systems PDF Download
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Author: Jacques Blanc-Talon
Publisher: Nova Publishers
ISBN: 9781590330333
Category : Computers
Languages : en
Pages : 326
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Book Description
Imaging & Vision Systems - Theory, Assessment & Applications, Advances in Computation, Theory & Practice -- Volume 9
Author: Jacques Blanc-Talon
Publisher: Nova Publishers
ISBN: 9781590330333
Category : Computers
Languages : en
Pages : 326
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Book Description
Imaging & Vision Systems - Theory, Assessment & Applications, Advances in Computation, Theory & Practice -- Volume 9
Author: Mohamed Elgendy
Publisher: Manning Publications
ISBN: 1617296198
Category : Computers
Languages : en
Pages : 478
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Book Description
How does the computer learn to understand what it sees? Deep Learning for Vision Systems answers that by applying deep learning to computer vision. Using only high school algebra, this book illuminates the concepts behind visual intuition. You'll understand how to use deep learning architectures to build vision system applications for image generation and facial recognition. Summary Computer vision is central to many leading-edge innovations, including self-driving cars, drones, augmented reality, facial recognition, and much, much more. Amazing new computer vision applications are developed every day, thanks to rapid advances in AI and deep learning (DL). Deep Learning for Vision Systems teaches you the concepts and tools for building intelligent, scalable computer vision systems that can identify and react to objects in images, videos, and real life. With author Mohamed Elgendy's expert instruction and illustration of real-world projects, you’ll finally grok state-of-the-art deep learning techniques, so you can build, contribute to, and lead in the exciting realm of computer vision! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology How much has computer vision advanced? One ride in a Tesla is the only answer you’ll need. Deep learning techniques have led to exciting breakthroughs in facial recognition, interactive simulations, and medical imaging, but nothing beats seeing a car respond to real-world stimuli while speeding down the highway. About the book How does the computer learn to understand what it sees? Deep Learning for Vision Systems answers that by applying deep learning to computer vision. Using only high school algebra, this book illuminates the concepts behind visual intuition. You'll understand how to use deep learning architectures to build vision system applications for image generation and facial recognition. What's inside Image classification and object detection Advanced deep learning architectures Transfer learning and generative adversarial networks DeepDream and neural style transfer Visual embeddings and image search About the reader For intermediate Python programmers. About the author Mohamed Elgendy is the VP of Engineering at Rakuten. A seasoned AI expert, he has previously built and managed AI products at Amazon and Twilio. Table of Contents PART 1 - DEEP LEARNING FOUNDATION 1 Welcome to computer vision 2 Deep learning and neural networks 3 Convolutional neural networks 4 Structuring DL projects and hyperparameter tuning PART 2 - IMAGE CLASSIFICATION AND DETECTION 5 Advanced CNN architectures 6 Transfer learning 7 Object detection with R-CNN, SSD, and YOLO PART 3 - GENERATIVE MODELS AND VISUAL EMBEDDINGS 8 Generative adversarial networks (GANs) 9 DeepDream and neural style transfer 10 Visual embeddings
Author: Pradip K. Sinha
Publisher: SPIE-International Society for Optical Engineering
ISBN: 9780819482020
Category : Computer vision
Languages : en
Pages : 0
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Book Description
Machine vision comprises three integrated processes: acquisition, preprocessing, and image analysis. While many resources discuss application-specific image analysis, there has been no unified account of image acquisition hardware and preprocessing - until now. This is a comprehensive, exhaustive reference text detailing every aspect of acquisition and preprocessing, from the illumination of a scene to the optics of image forming, from CCD and CMOS image capture to the transformation of the captured image.
Author: Anil Anthony Bharath
Publisher: Artech House
ISBN: 1596932252
Category : Computers
Languages : en
Pages : 453
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Book Description
This interdisciplinary work brings you to the cutting edge of emerging technologies inspired by human sight, ranging from semiconductor photoreceptors based on novel organic polymers and retinomorphic processing circuitry to low-powered devices that replicate spatial and temporal processing in the brain. Moreover, it is the first work of its kind that integrates the full range of physiological, engineering, and mathematical issues and advances together in a single source.
Author: Andreas Maier
Publisher: Springer
ISBN: 3319965204
Category : Computers
Languages : en
Pages : 263
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Book Description
This open access book gives a complete and comprehensive introduction to the fields of medical imaging systems, as designed for a broad range of applications. The authors of the book first explain the foundations of system theory and image processing, before highlighting several modalities in a dedicated chapter. The initial focus is on modalities that are closely related to traditional camera systems such as endoscopy and microscopy. This is followed by more complex image formation processes: magnetic resonance imaging, X-ray projection imaging, computed tomography, X-ray phase-contrast imaging, nuclear imaging, ultrasound, and optical coherence tomography.
Author: Bruce G. Batchelor
Publisher: Springer Science & Business Media
ISBN: 1447104315
Category : Computers
Languages : en
Pages : 475
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Book Description
The application of intelligent imaging techniques to industrial vision problems is an evolving aspect of current machine vision research. Machine vision is a relatively new technology, more concerned with systems engineering than with computer science, and with much to offer the manufacturing industry in terms of improving efficiency, safety and product quality. Beginning with an introductory chapter on the basic concepts, the authors develop these ideas to describe intelligent imaging techniques for use in a new generation of industrial imaging systems. Sections cover the application of AI languages such as Prolog, the use of multi-media interfaces and multi-processor systems, external device control, and colour recognition. The text concludes with a discussion of several case studies that illustrate how intelligent machine vision techniques can be used in industrial applications.
Author: Pradip K. Sinha
Publisher:
ISBN: 9781680150964
Category : Computer vision
Languages : en
Pages : 750
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Book Description
This book provides a combination of the operational details of imaging hardware and analytical theories of low-level image processing functions. By a blend of optics, stage lighting, and framegrabber descriptions, and detailed theories of CCD and CMOS image sensors, image formation, and camera calibration, the image acquisition part of the book provides a comprehensive reference text for image acquisition. The pre-processing part brings together a wide range of enhancement and filtering kernels and imaging functions through well-structured analytical bases. With unified coverage of image acquisition modules and pre-processing functions, this book bridges the gaps between hardware and software on one hand and theory and applications on the other. With its detailed coverage of imaging hardware and derivations of pre-processing kernels, it is a useful design reference for students, researchers, application and product engineers, and systems integrators.
Author: Zheng Liu
Publisher: Springer
ISBN: 1447167414
Category : Computers
Languages : en
Pages : 541
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Book Description
This pioneering text/reference presents a detailed focus on the use of machine vision techniques in industrial inspection applications. An internationally renowned selection of experts provide insights on a range of inspection tasks, drawn from their cutting-edge work in academia and industry, covering practical issues of vision system integration for real-world applications. Topics and features: presents a comprehensive review of state-of-the-art hardware and software tools for machine vision, and the evolution of algorithms for industrial inspection; includes in-depth descriptions of advanced inspection methodologies and machine vision technologies for specific needs; discusses the latest developments and future trends in imaging and vision techniques for industrial inspection tasks; provides a focus on imaging and vision system integration, implementation, and optimization; describes the pitfalls and barriers to developing successful inspection systems for smooth and efficient manufacturing process.
Author: Vladan Popovic
Publisher: Springer
ISBN: 331959057X
Category : Technology & Engineering
Languages : en
Pages : 263
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Book Description
This book discusses the design of multi-camera systems and their application to fields such as the virtual reality, gaming, film industry, medicine, automotive industry, drones, etc. The authors cover the basics of image formation, algorithms for stitching a panoramic image from multiple cameras, and multiple real-time hardware system architectures, in order to have panoramic videos. Several specific applications of multi-camera systems are presented, such as depth estimation, high dynamic range imaging, and medical imaging.
Author: Bart M. Haar Romeny
Publisher: Springer Science & Business Media
ISBN: 140208840X
Category : Computers
Languages : en
Pages : 470
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Book Description
Many approaches have been proposed to solve the problem of finding the optic flow field of an image sequence. Three major classes of optic flow computation techniques can discriminated (see for a good overview Beauchemin and Barron IBeauchemin19951): gradient based (or differential) methods; phase based (or frequency domain) methods; correlation based (or area) methods; feature point (or sparse data) tracking methods; In this chapter we compute the optic flow as a dense optic flow field with a multi scale differential method. The method, originally proposed by Florack and Nielsen [Florack1998a] is known as the Multiscale Optic Flow Constrain Equation (MOFCE). This is a scale space version of the well known computer vision implementation of the optic flow constraint equation, as originally proposed by Horn and Schunck [Horn1981]. This scale space variation, as usual, consists of the introduction of the aperture of the observation in the process. The application to stereo has been described by Maas et al. [Maas 1995a, Maas 1996a]. Of course, difficulties arise when structure emerges or disappears, such as with occlusion, cloud formation etc. Then knowledge is needed about the processes and objects involved. In this chapter we focus on the scale space approach to the local measurement of optic flow, as we may expect the visual front end to do. 17. 2 Motion detection with pairs of receptive fields As a biologically motivated start, we begin with discussing some neurophysiological findings in the visual system with respect to motion detection.
