Computer Vision: Craft, Engineering, and Science, David Vernon
Автор: Strang Gilbert Название: Linear Algebra and Learning from Data ISBN: 0692196382 ISBN-13(EAN): 9780692196380 Издательство: Cambridge Academ Рейтинг: Цена: 9978.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.
Without mathematics no science would survive. This especially applies to the engineering sciences which highly depend on the applications of mathematics and mathematical tools such as optimization techniques, finite element methods, differential equations, fluid dynamics, mathematical modelling, and simulation. Neither optimization in engineering, nor the performance of safety-critical system and system security; nor high assurance software architecture and design would be possible without the development of mathematical applications.
De Gruyter Series on the Applications of Mathematics in Engineering and Information Sciences (AMEIS) focusses on the latest applications of engineering and information technology that are possible only with the use of mathematical methods. By identifying the gaps in knowledge of engineering applications the AMEIS series fosters the international interchange between the sciences and keeps the reader informed about the latest developments.
Автор: Amir R. Zamir et al Название: Large-Scale Visual Geo-Localization ISBN: 331925779X ISBN-13(EAN): 9783319257792 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Нет в наличии.
Описание: Thistimely and authoritative volume explores the bidirectional relationship betweenimages and locations. The text presents a comprehensive review of the state ofthe art in large-scale visual geo-localization, and discusses the emergingtrends in this area. Valuable insights are supplied by a pre-eminent selectionof experts in the field, into a varied range of real-world applications ofgeo-localization. Topics and features: discusses the latest methods to exploitinternet-scale image databases for devising geographically rich features andgeo-localizing query images at different scales; investigates geo-localizationtechniques that are built upon high-level and semantic cues; describes methodsthat perform precise localization by geometrically aligning the query imageagainst a 3D model; reviews techniques that accomplish image understandingassisted by the geo-location, as well as several approaches for geo-localizationunder practical, real-world settings.
This book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering. By only assuming a knowledge of calculus, the authors develop, in a rigorous yet down to earth manner, the mathematical theory behind concepts such as: vectors spaces, bases, linear maps, duality, Hermitian spaces, the spectral theorems, SVD, and the primary decomposition theorem. At all times, pertinent real-world applications are provided. This book includes the mathematical explanations for the tools used which we believe that is adequate for computer scientists, engineers and mathematicians who really want to do serious research and make significant contributions in their respective fields.
Автор: Julio C. Rodriguez-Quinonez, Oleg Sergiyenko, Wendy Flores-Fuentes Название: Examining Optoelectronics in Machine Vision and Applications in Industry 4.0 ISBN: 1799865231 ISBN-13(EAN): 9781799865230 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 26334.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Focuses on the examination of emerging technologies for the design, fabrication, and implementation of optoelectronic sensors, devices, and systems in a machine vision approach to support industrial, commercial, and scientific applications.
Автор: E. R. Davies Название: Advanced Methods and Deep Learning in Computer Vision ISBN: 0128221097 ISBN-13(EAN): 9780128221099 Издательство: Elsevier Science Рейтинг: Цена: 16505.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasizing machine and deep learning techniques that have emerged during the past 5-10 years. The book provides clear explanations of principles and algorithms supported with applications. Topics covered include machine learning, deep learning networks, generative adversarial networks, deep reinforcement learning, self-supervised learning, extraction of robust features, object detection, semantic segmentation, linguistic descriptions of images, visual search, visual tracking, 3D shape retrieval, image inpainting, novelty and anomaly detection.
This book provides easy learning for researchers and practitioners of advanced computer vision methods, but it is also suitable as a textbook for a second course on computer vision and deep learning for advanced undergraduates and graduate students.
Автор: Diego Alexander Tibaduiza Burgos, Maribel Anaya Ve Название: Pattern Recognition Applications in Engineering ISBN: 1799818403 ISBN-13(EAN): 9781799818403 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 23839.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The implementation of data and information analysis has become a trending solution within multiple professions. New tools and approaches are continually being developed within data analysis to further solve the challenges that come with professional strategy. Pattern recognition is an innovative method that provides comparison techniques and defines new characteristics within the information acquisition process. Despite its recent trend, a considerable amount of research regarding pattern recognition and its various strategies is lacking. Pattern Recognition Applications in Engineering is an essential reference source that discusses various strategies of pattern recognition algorithms within industrial and research applications and provides examples of results in different professional areas including electronics, computation, and health monitoring. Featuring research on topics such as condition monitoring, data normalization, and bio-inspired developments, this book is ideally designed for analysts; researchers; civil, mechanical, and electronic engineers; computing scientists; chemists; academicians; and students.
Автор: Chen, Mei Название: Computer Vision for Microscopy Image Analysis ISBN: 0128149728 ISBN-13(EAN): 9780128149720 Издательство: Elsevier Science Рейтинг: Цена: 17854.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
High-throughput microscopy enables researchers to acquire thousands of images automatically over a short time, making it possible to conduct large-scale, image-based experiments for biological or biomedical discovery. However, visual analysis of large-scale image data is a daunting task. The post-acquisition component of high-throughput microscopy experiments calls for effective and efficient computer vision techniques.
Computer Vision for Microscopy Image Analysis provides a comprehensive and in-depth introduction to state-of-the-art computer vision techniques for microscopy image analysis, demonstrating how they can be effectively applied to biological and medical data.
The reader of the book will learn:
How computer vision analysis can automate and enhance human assessment of microscopy images for discovery
The important steps in microscopy image analysis
State-of-the-art methods for microscopy image analysis including machine learning and deep neural network approaches
This reference on the state-of-the-art computer vision methods in microscopy image analysis is suitable for researchers and graduate students interested in analyzing microscopy images or for developing toolsets for general biomedical image analysis applications.
Each topic contains a comprehensive overview of the field, followed by in-depth presentation of a state-of-the-art approach
Perspectives and content contributed by both technologists and biologists
Tackles specific problems of detection, segmentation, classification, tracking, cellular event detection
Contains the fundamentals of object measurement in microscopy images
Contains open source data and toolsets for microscopy image analysis on an accompanying website
Автор: Diego Alexander et al Название: Pattern Recognition Applications in Engineering ISBN: 179981839X ISBN-13(EAN): 9781799818397 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 28967.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The implementation of data and information analysis has become a trending solution within multiple professions. New tools and approaches are continually being developed within data analysis to further solve the challenges that come with professional strategy. Pattern recognition is an innovative method that provides comparison techniques and defines new characteristics within the information acquisition process. Despite its recent trend, a considerable amount of research regarding pattern recognition and its various strategies is lacking.
Pattern Recognition Applications in Engineering is an essential reference source that discusses various strategies of pattern recognition algorithms within industrial and research applications and provides examples of results in different professional areas including electronics, computation, and health monitoring. Featuring research on topics such as condition monitoring, data normalization, and bio-inspired developments, this book is ideally designed for analysts; researchers; civil, mechanical, and electronic engineers; computing scientists; chemists; academicians; and students.
Автор: Marius Leordeanu Название: Unsupervised Learning in Space and Time ISBN: 3030421279 ISBN-13(EAN): 9783030421274 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book addresses one of the most important unsolved problems in artificial intelligence: the task of learning, in an unsupervised manner, from massive quantities of spatiotemporal visual data that are available at low cost. The book covers important scientific discoveries and findings, with a focus on the latest advances in the field. Presenting a coherent structure, the book logically connects novel mathematical formulations and efficient computational solutions for a range of unsupervised learning tasks, including visual feature matching, learning and classification, object discovery, and semantic segmentation in video.
The final part of the book proposes a general strategy for visual learning over several generations of student-teacher neural networks, along with a unique view on the future of unsupervised learning in real-world contexts. Offering a fresh approach to this difficult problem, several efficient, state-of-the-art unsupervised learning algorithms are reviewed in detail, complete with an analysis of their performance on various tasks, datasets, and experimental setups. By highlighting the interconnections between these methods, many seemingly diverse problems are elegantly brought together in a unified way.
Serving as an invaluable guide to the computational tools and algorithms required to tackle the exciting challenges in the field, this book is a must-read for graduate students seeking a greater understanding of unsupervised learning, as well as researchers in computer vision, machine learning, robotics, and related disciplines.
Автор: Hartley, Zisserman Название: Multiple View Geometry in Computer Vision ISBN: 0521540518 ISBN-13(EAN): 9780521540513 Издательство: Cambridge Academ Рейтинг: Цена: 13779.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The theory and practice of scene reconstruction are described in detail in a unified framework. The new edition features an extended introduction covering the key ideas in the book (which itself has been updated with additional examples and appendices) and significant new results which have appeared since the first edition.
Описание: Machine learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers. Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and image processing. There is a need for research that seeks to understand the development and efficiency of current methods that enable machines to see. Challenges and Applications for Implementing Machine Learning in Computer Vision is a collection of innovative research that combines theory and practice on adopting the latest deep learning advancements for machines capable of visual processing. Highlighting a wide range of topics such as video segmentation, object recognition, and 3D modelling, this publication is ideally designed for computer scientists, medical professionals, computer engineers, information technology practitioners, industry experts, scholars, researchers, and students seeking current research on the utilization of evolving computer vision techniques.
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