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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

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
Release Date : 2024-01-13

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, and advances 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: 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.

Deep Learning In Computer Vision

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Author : Mahmoud Hassaballah
language : en
Publisher: CRC Press
Release Date : 2020-03-23

Deep Learning In Computer Vision written by Mahmoud Hassaballah and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-23 with Computers categories.

Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unsolved or partially addressed. This book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision community. The book covers a broad scope of topics in deep learning concepts and applications such as accelerating the convolutional neural network inference on field-programmable gate arrays, fire detection in surveillance applications, face recognition, action and activity recognition, semantic segmentation for autonomous driving, aerial imagery registration, robot vision, tumor detection, and skin lesion segmentation as well as skin melanoma classification. The content of this book has been organized such that each chapter can be read independently from the others. The book is a valuable companion for researchers, for postgraduate and possibly senior undergraduate students who are taking an advanced course in related topics, and for those who are interested in deep learning with applications in computer vision, image processing, and pattern recognition.

Group And Crowd Behavior For Computer Vision

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Author : Vittorio Murino
language : en
Publisher: Academic Press
Release Date : 2017-04-18

Group And Crowd Behavior For Computer Vision written by Vittorio Murino and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-18 with Computers categories.

Group and Crowd Behavior for Computer Vision provides a multidisciplinary perspective on how to solve the problem of group and crowd analysis and modeling, combining insights from the social sciences with technological ideas in computer vision and pattern recognition. The book answers many unresolved issues in group and crowd behavior, with Part One providing an introduction to the problems of analyzing groups and crowds that stresses that they should not be considered as completely diverse entities, but as an aggregation of people. Part Two focuses on features and representations with the aim of recognizing the presence of groups and crowds in image and video data. It discusses low level processing methods to individuate when and where a group or crowd is placed in the scene, spanning from the use of people detectors toward more ad-hoc strategies to individuate group and crowd formations. Part Three discusses methods for analyzing the behavior of groups and the crowd once they have been detected, showing how to extract semantic information, predicting/tracking the movement of a group, the formation or disaggregation of a group/crowd and the identification of different kinds of groups/crowds depending on their behavior. The final section focuses on identifying and promoting datasets for group/crowd analysis and modeling, presenting and discussing metrics for evaluating the pros and cons of the various models and methods. This book gives computer vision researcher techniques for segmentation and grouping, tracking and reasoning for solving group and crowd modeling and analysis, as well as more general problems in computer vision and machine learning. Presents the first book to cover the topic of modeling and analysis of groups in computer vision Discusses the topics of group and crowd modeling from a cross-disciplinary perspective, using social science anthropological theories translated into computer vision algorithms Focuses on group and crowd analysis metrics Discusses real industrial systems dealing with the problem of analyzing groups and crowds

Metric Learning

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Author : Aurélien Muise
language : en
Publisher: Springer Nature
Release Date : 2022-05-31

Metric Learning written by Aurélien Muise and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.

Similarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern recognition and data mining methods. This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years. In this book, we provide a thorough review of the metric learning literature that covers algorithms, theory and applications for both numerical and structured data. We first introduce relevant definitions and classic metric functions, as well as examples of their use in machine learning and data mining. We then review a wide range of metric learning algorithms, starting with the simple setting of linear distance and similarity learning. We show how one may scale-up these methods to very large amounts of training data. To go beyond the linear case, we discuss methods that learn nonlinear metrics or multiple linear metrics throughout the feature space, and review methods for more complex settings such as multi-task and semi-supervised learning. Although most of the existing work has focused on numerical data, we cover the literature on metric learning for structured data like strings, trees, graphs and time series. In the more technical part of the book, we present some recent statistical frameworks for analyzing the generalization performance in metric learning and derive results for some of the algorithms presented earlier. Finally, we illustrate the relevance of metric learning in real-world problems through a series of successful applications to computer vision, bioinformatics and information retrieval. Table of Contents: Introduction / Metrics / Properties of Metric Learning Algorithms / Linear Metric Learning / Nonlinear and Local Metric Learning / Metric Learning for Special Settings / Metric Learning for Structured Data / Generalization Guarantees for Metric Learning / Applications / Conclusion / Bibliography / Authors' Biographies

Full Reference Image Quality Metrics

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Author : Marius Pedersen
language : en
Publisher: Now Pub
Release Date : 2012-04-01

Full Reference Image Quality Metrics written by Marius Pedersen and has been published by Now Pub this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-04-01 with Computers categories.

This text surveys one full-reference image quality metrics. It starts by classifying these image quality metrics into four different groups and then selects further image quality metrics from each group and evaluates them against six state-of-the-art image quality databases.

Performance Characterization In Computer Vision

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Author : Reinhard Klette
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-04-17

Performance Characterization In Computer Vision written by Reinhard Klette 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 2013-04-17 with Computers categories.

This edited volume addresses a subject which has been discussed inten sively in the computer vision community for several years. Performance characterization and evaluation of computer vision algorithms are of key importance, particularly with respect to the configuration of reliable and ro bust computer vision systems as well as the dissemination of reconfigurable systems in novel application domains. Although a plethora of literature on this subject is available for certain' areas of computer vision, the re search community still faces a lack of a well-grounded, generally accepted, and--eventually-standardized methods. The range of fundamental problems encoIl!passes the value of synthetic images in experimental computer vision, the selection of a representative set of real images related to specific domains and tasks, the definition of ground truth given different tasks and applications, the design of experimental test beds, the analysis of algorithms with respect to general characteristics such as complexity, resource consumption, convergence, stability, or range of admissible input data, the definition and analysis of performance measures for classes of algorithms, the role of statistics-based performance measures, the generation of data sheets with performance measures of algorithms sup porting the system engineer in his configuration problem, and the validity of model assumptions for specific applications of computer vision.

Computer Vision Metrics

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