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Computer Vision Methods for Fast Image Classification and Retrieval, Rafal Scherer


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Цена: 13974.00р.
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Автор: Rafal Scherer
Название:  Computer Vision Methods for Fast Image Classification and Retrieval
Перевод названия: Рафаль Шерер: Методы машинного зрения для быстрой классификации и извлечения изображений
ISBN: 9783030121945
Издательство: Springer
Классификация:



ISBN-10: 3030121941
Обложка/Формат: Hardcover
Страницы: 137
Вес: 0.40 кг.
Дата издания: 2020
Серия: Studies in Computational Intelligence
Язык: English
Издание: 1st ed. 2020
Иллюстрации: 55 illustrations, color; 30 illustrations, black and white; ix, 137 p. 85 illus., 55 illus. in color.
Размер: 234 x 156 x 10
Читательская аудитория: Professional & vocational
Основная тема: Engineering
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание:
The book presents selected methods for accelerating image retrieval and classification in large collections of images using what are referred to as ‘hand-crafted features.’ It introduces readers to novel rapid image description methods based on local and global features, as well as several techniques for comparing images.
Developing content-based image comparison, retrieval and classification methods that simulate human visual perception is an arduous and complex process. The book’s main focus is on the application of these methods in a relational database context. The methods presented are suitable for both general-type and medical images. Offering a valuable textbook for upper-level undergraduate or graduate-level courses on computer science or engineering, as well as a guide for computer vision researchers, the book focuses on techniques that work under real-world large-dataset conditions.

Дополнительное описание: Preface.- Chapter 1. Introduction.- Chapter 2. Feature Detection.- Chapter 3. Image Indexing Techniques.- Chapter 4. Novel Methods for Image Description.- Chapter 5. Image Retrieval and Classi?cation in Relational Databases etc.



Partial Differential Equation Methods for Image Inpainting

Автор: Schоnlieb
Название: Partial Differential Equation Methods for Image Inpainting
ISBN: 1107001005 ISBN-13(EAN): 9781107001008
Издательство: Cambridge Academ
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Цена: 12195.00 р.
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Описание: This book is concerned with digital image processing techniques that use partial differential equations (PDEs) for the task of image 'inpainting', an artistic term for virtual image restoration or interpolation, whereby missing or occluded parts in images are completed based on information provided by intact parts. Computer graphic designers, artists and photographers have long used manual inpainting to restore damaged paintings or manipulate photographs. Today, mathematicians apply powerful methods based on PDEs to automate this task. This book introduces the mathematical concept of PDEs for virtual image restoration. It gives the full picture, from the first modelling steps originating in Gestalt theory and arts restoration to the analysis of resulting PDE models, numerical realisation and real-world application. This broad approach also gives insight into functional analysis, variational calculus, optimisation and numerical analysis and will appeal to researchers and graduate students in mathematics with an interest in image processing and mathematical analysis.

Scale Space and Variational Methods in Computer Vision

Автор: Xue-Cheng Tai; Knut Morken; Marius Lysaker; Knut-A
Название: Scale Space and Variational Methods in Computer Vision
ISBN: 3642022553 ISBN-13(EAN): 9783642022555
Издательство: Springer
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Цена: 19564.00 р.
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Описание: This book contains 71 original, scienti?c articles that address state-of-the-art researchrelatedto scale space and variationalmethods for image processing and computer vision.

Information Retrieval Methods For Multidisciplinary Applications

Автор: Lu
Название: Information Retrieval Methods For Multidisciplinary Applications
ISBN: 1466638982 ISBN-13(EAN): 9781466638983
Издательство: Mare Nostrum (Eurospan)
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Цена: 25502.00 р.
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Описание: The internet provides a vast amount of data which can be utilised to explore different approaches to solving industry problems. Efficient methods for the retrieval of this information are essential for streamlined business processes. <em>Information Retrieval Methods for Multidisciplinary Applications</em> provides innovative research on information gathering, web data mining, and automation systems. Addressing multidisciplinary applications and focusing on theories and methods with an enterprise-wide perspective, this book is essential for information engineers, scientists, and related professionals.

Advances in Digital Document Processing and Retrieval

Автор: B B Chaudhuri
Название: Advances in Digital Document Processing and Retrieval
ISBN: 9814368709 ISBN-13(EAN): 9789814368704
Издательство: World Scientific Publishing
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Цена: 17741.00 р.
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Описание: Suitable for both students and researchers working on various aspects of document image analysis and recognition problems, this title covers such topics as: Going beyond the Myth of Paperlessness, The Role of Document Image Analysis in Trustworthy Elections as well as Word Recognition for Museum Index Cards with SNT-Grid.

Extreme Value Theory-Based Methods for Visual Recognition

Автор: Walter J. Scheirer
Название: Extreme Value Theory-Based Methods for Visual Recognition
ISBN: 1627057005 ISBN-13(EAN): 9781627057004
Издательство: Turpin
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Цена: 10340.00 р.
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Описание: A common feature of many approaches to modeling sensory statistics is an emphasis on capturing the ""average."" From early representations in the brain, to highly abstracted class categories in machine learning for classification tasks, central-tendency models based on the Gaussian distribution are a seemingly natural and obvious choice for modeling sensory data. However, insights from neuroscience, psychology, and computer vision suggest an alternate strategy: preferentially focusing representational resources on the extremes of the distribution of sensory inputs. The notion of treating extrema near a decision boundary as features is not necessarily new, but a comprehensive statistical theory of recognition based on extrema is only now just emerging in the computer vision literature. This book begins by introducing the statistical Extreme Value Theory (EVT) for visual recognition. In contrast to central-tendency modeling, it is hypothesized that distributions near decision boundaries form a more powerful model for recognition tasks by focusing coding resources on data that are arguably the most diagnostic features. EVT has several important properties: strong statistical grounding, better modeling accuracy near decision boundaries than Gaussian modeling, the ability to model asymmetric decision boundaries, and accurate prediction of the probability of an event beyond our experience. The second part of the book uses the theory to describe a new class of machine learning algorithms for decision making that are a measurable advance beyond the state-of-the-art. This includes methods for post-recognition score analysis, information fusion, multi-attribute spaces, and calibration of supervised machine learning algorithms.

Scale Space and Variational Methods in Computer Vision

Автор: Jean-Fran?ois Aujol; Mila Nikolova; Nicolas Papada
Название: Scale Space and Variational Methods in Computer Vision
ISBN: 3319184601 ISBN-13(EAN): 9783319184609
Издательство: Springer
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Цена: 11180.00 р.
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Описание: This book constitutes the refereed proceedings of the 5th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2015, held in Lege-Cap Ferret, France, in May 2015. The papers are organized in the following topical sections: scale space and partial differential equation methods;

Scale Space and Variational Methods in Computer Vision

Автор: Jan Lellmann; Martin Burger; Jan Modersitzki
Название: Scale Space and Variational Methods in Computer Vision
ISBN: 3030223671 ISBN-13(EAN): 9783030223670
Издательство: Springer
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Цена: 6986.00 р.
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Описание: This book constitutes the proceedings of the 7th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2019, held in Hofgeismar, Germany, in June/July 2019.

The 44 papers included in this volume were carefully reviewed and selected for inclusion in this book. They were organized in topical sections named: 3D vision and feature analysis; inpainting, interpolation and compression; inverse problems in imaging; optimization methods in imaging; PDEs and level-set methods; registration and reconstruction; scale-space methods; segmentation and labeling; and variational methods.
Energy Minimization Methods in Computer Vision and Pattern Recognition

Автор: Anders Heyden; Fredrik Kahl; Carl Olsson; Magnus O
Название: Energy Minimization Methods in Computer Vision and Pattern Recognition
ISBN: 3642403948 ISBN-13(EAN): 9783642403941
Издательство: Springer
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Цена: 6986.00 р.
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Описание: This volume constitutes the refereed proceedings of the 9th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2013, held in Lund, Sweden, in August 2013. The papers are organized in topical sections on Medical Imaging;


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