Computer Vision Metrics
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Computer Vision Metrics
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Author : Scott Krig
language : en
Publisher: Apress
Release Date : 2014-06-14
Computer Vision Metrics written by Scott Krig and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-14 with Computers categories.
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.
Computer Vision Metrics
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Author : Scott Krig
language : en
Publisher: Springer
Release Date : 2016-09-16
Computer Vision Metrics written by Scott Krig and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-16 with Computers categories.
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.
Computer Vision Metrics
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Author : Scott Krig
language : en
Publisher: Springer
Release Date : 2016-07-07
Computer Vision Metrics written by Scott Krig and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-07 with Computers categories.
This second edition provides a comprehensive history and state-of-the-art survey for fundamental computer vision methods. Expanded and updated, this book features over 300 new references, totaling over 800 in all, as well as learning assignments at the end of each chapter to help students and researchers dig deeper into key topics. This survey covers everything from imaging devices, computational imaging, interest point detectors, local 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 book includes useful analysis to provide intuition into the goals of various methods, why they work, and how they may be optimized. This is not a how-to book with source code examples, but rather a survey and taxonomy intended as a reference tool for researchers and engineers, complimenting the many fine hand-on resources and open source projects such as OpenCV and other imaging and deep learning tools.
Computer Vision Metrics
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Author : Scott Krig
language : en
Publisher: Springer Nature
Release Date : 2025-05-18
Computer Vision Metrics written by Scott Krig and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-18 with Computers categories.
This 2nd Edition, based on the successful 2016 textbook, has been updated and expanded to cover 3rd generation Computer Vision and AI as it supersedes historical visual computing methods, providing a comprehensive survey of essential topics and methods in Computer Vision. With over 1,200 essential references, as well as chapter-by-chapter learning assignments, the book offers a valuable resource for students, researchers, scientists and engineers, helping them dig deeper into core computer vision and foundational visual computing and neuroscience topics. As before, a historical survey of advances in Computer Vision is provided, updated to reflect the latest methods such as Vision Transformers, attention models, alternative features such as Fourier neurons and Binary neurons, hybrid DNN architectures, self-supervised and enhanced learning models, Associative Multimodal Learning, Continuous Learning, View Synthesis, intelligent Scientific Imaging, andadvances in training protocols. Updates have also been added for 2d/3d cameras, software libraries and open source resources, computer vision cloud services, and vision/AI hardware accelerators. Discussion and analysis are provided to uncover intuition and delve into the essence of key advancements, applied and forward-looking topics.
Perceptual Metrics For Image Database Navigation
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Author : Yossi Rubner
language : en
Publisher:
Release Date : 1999
Perceptual Metrics For Image Database Navigation written by Yossi Rubner and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Database management categories.
Perceptual Metrics For Image Database Navigation
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Author : Yossi Rubner
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-14
Perceptual Metrics For Image Database Navigation written by Yossi Rubner and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-14 with Technology & Engineering categories.
The increasing amount of information available in today's world raises the need to retrieve relevant data efficiently. Unlike text-based retrieval, where keywords are successfully used to index into documents, content-based image retrieval poses up front the fundamental questions how to extract useful image features and how to use them for intuitive retrieval. We present a novel approach to the problem of navigating through a collection of images for the purpose of image retrieval, which leads to a new paradigm for image database search. We summarize the appearance of images by distributions of color or texture features, and we define a metric between any two such distributions. This metric, which we call the "Earth Mover's Distance" (EMD), represents the least amount of work that is needed to rearrange the mass is one distribution in order to obtain the other. We show that the EMD matches perceptual dissimilarity better than other dissimilarity measures, and argue that it has many desirable properties for image retrieval. Using this metric, we employ Multi-Dimensional Scaling techniques to embed a group of images as points in a two- or three-dimensional Euclidean space so that their distances reflect image dissimilarities as well as possible. Such geometric embeddings exhibit the structure in the image set at hand, allowing the user to understand better the result of a database query and to refine the query in a perceptually intuitive way.
Image Understanding Workshop
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Author : United States. Defense Advanced Research Projects Agency. Information Science and Technology Office
language : en
Publisher: Morgan Kaufmann
Release Date : 1988
Image Understanding Workshop written by United States. Defense Advanced Research Projects Agency. Information Science and Technology Office and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988 with Computers categories.
"The main theme of the 1988 workshop, the 18th in this DARPA sponsored series of meetings on Image Understanding and Computer Vision, is to cover new vision techniques in prototype vision systems for manufacturing, navigation, cartography, and photointerpretation." P. v.
Perceptual Metrics For Video Quality Evaluation And Image Watermarking
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Author : Mark Andrew Masry
language : en
Publisher: Ann Arbor, Mich. : University Microfilms International
Release Date : 2004
Perceptual Metrics For Video Quality Evaluation And Image Watermarking written by Mark Andrew Masry and has been published by Ann Arbor, Mich. : University Microfilms International this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
New Approximation Algorithms And Structural Results For Oblivious Multicommodity Flow And Zero Extension
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Author : Chris Robert Harrelson
language : en
Publisher:
Release Date : 2004
New Approximation Algorithms And Structural Results For Oblivious Multicommodity Flow And Zero Extension written by Chris Robert Harrelson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
Deep Learning With Jax
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Author : Grigory Sapunov
language : en
Publisher: Simon and Schuster
Release Date : 2024-12-03
Deep Learning With Jax written by Grigory Sapunov and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-03 with Computers categories.
Accelerate deep learning and other number-intensive tasks with JAX, Google’s awesome high-performance numerical computing library. The JAX numerical computing library tackles the core performance challenges at the heart of deep learning and other scientific computing tasks. By combining Google’s Accelerated Linear Algebra platform (XLA) with a hyper-optimized version of NumPy and a variety of other high-performance features, JAX delivers a huge performance boost in low-level computations and transformations. In Deep Learning with JAX you will learn how to: • Use JAX for numerical calculations • Build differentiable models with JAX primitives • Run distributed and parallelized computations with JAX • Use high-level neural network libraries such as Flax • Leverage libraries and modules from the JAX ecosystem Deep Learning with JAX is a hands-on guide to using JAX for deep learning and other mathematically-intensive applications. Google Developer Expert Grigory Sapunov steadily builds your understanding of JAX’s concepts. The engaging examples introduce the fundamental concepts on which JAX relies and then show you how to apply them to real-world tasks. You’ll learn how to use JAX’s ecosystem of high-level libraries and modules, and also how to combine TensorFlow and PyTorch with JAX for data loading and deployment. About the technology Google’s JAX offers a fresh vision for deep learning. This powerful library gives you fine control over low level processes like gradient calculations, delivering fast and efficient model training and inference, especially on large datasets. JAX has transformed how research scientists approach deep learning. Now boasting a robust ecosystem of tools and libraries, JAX makes evolutionary computations, federated learning, and other performance-sensitive tasks approachable for all types of applications. About the book Deep Learning with JAX teaches you to build effective neural networks with JAX. In this example-rich book, you’ll discover how JAX’s unique features help you tackle important deep learning performance challenges, like distributing computations across a cluster of TPUs. You’ll put the library into action as you create an image classification tool, an image filter application, and other realistic projects. The nicely-annotated code listings demonstrate how JAX’s functional programming mindset improves composability and parallelization. What's inside • Use JAX for numerical calculations • Build differentiable models with JAX primitives • Run distributed and parallelized computations with JAX • Use high-level neural network libraries such as Flax About the reader For intermediate Python programmers who are familiar with deep learning. About the author Grigory Sapunov holds a Ph.D. in artificial intelligence and is a Google Developer Expert in Machine Learning. The technical editor on this book was Nicholas McGreivy. Table of Contents Part 1 1 When and why to use JAX 2 Your first program in JAX Part 2 3 Working with arrays 4 Calculating gradients 5 Compiling your code 6 Vectorizing your code 7 Parallelizing your computations 8 Using tensor sharding 9 Random numbers in JAX 10 Working with pytrees Part 3 11 Higher-level neural network libraries 12 Other members of the JAX ecosystem A Installing JAX B Using Google Colab C Using Google Cloud TPUs D Experimental parallelization