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Publicado por Cham, Springer International Publishing : Imprint: Springer., 2021
ISBN 10: 3030692507 ISBN 13: 9783030692506
Idioma: Inglés
Librería: Universitätsbuchhandlung Herta Hold GmbH, Berlin, Alemania
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Añadir al carrito2nd ed. 2021. XXXIII, 363 p. Hardcover. 2nd ed. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Texts in Computer Science. Sprache: Englisch.
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Publicado por Springer Nature Switzerland AG, 2022
ISBN 10: 3030692531 ISBN 13: 9783030692537
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Añadir al carritoPaperback or Softback. Condición: New. Fundamentals of Image Data Mining: Analysis, Features, Classification and Retrieval. Book.
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Publicado por Springer Nature Switzerland AG, 2022
ISBN 10: 3030692531 ISBN 13: 9783030692537
Idioma: Inglés
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Publicado por Springer Nature Switzerland AG, Cham, 2022
ISBN 10: 3030692531 ISBN 13: 9783030692537
Idioma: Inglés
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Añadir al carritoPaperback. Condición: new. Paperback. This unique and useful textbook presents a comprehensive review of the essentials of image data mining, and the latest cutting-edge techniques used in the field. The coverage spans all aspects of image analysis and understanding, offering deep insights into areas of feature extraction, machine learning, and image retrieval. The theoretical coverage is supported by practical mathematical models and algorithms, utilizing data from real-world examples and experiments. Topics and features: Describes essential tools for image mining, covering Fourier transforms, Gabor filters, and contemporary wavelet transformsDevelops many new exercises (most with MATLAB code and instructions)Includes review summaries at the end of each chapterAnalyses state-of-the-art models, algorithms, and procedures for image miningIntegrates new sections on pre-processing, discrete cosine transform, and statistical inference and testingDemonstrates how features like color, texture, and shape can be mined or extracted for image representationApplies powerful classification approaches: Bayesian classification, support vector machines, neural networks, and decision treesImplements imaging techniques for indexing, ranking, and presentation, as well as database visualization This easy-to-follow, award-winning book illuminates how concepts from fundamental and advanced mathematics can be applied to solve a broad range of image data mining problems encountered by students and researchers of computer science. Students of mathematics and other scientific disciplines will also benefit from the applications and solutions described in the text, together with the hands-on exercises that enable the reader to gain first-hand experience of computing. Analysis Features Classification and Retrieval. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Publicado por Springer Nature Switzerland AG, Cham, 2020
ISBN 10: 3030179915 ISBN 13: 9783030179915
Idioma: Inglés
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Añadir al carritoPaperback. Condición: new. Paperback. This reader-friendly textbook presents a comprehensive review of the essentials of image data mining, and the latest cutting-edge techniques used in the field. The coverage spans all aspects of image analysis and understanding, offering deep insights into areas of feature extraction, machine learning, and image retrieval. The theoretical coverage is supported by practical mathematical models and algorithms, utilizing data from real-world examples and experiments.Topics and features: describes the essential tools for image mining, covering Fourier transforms, Gabor filters, and contemporary wavelet transforms; reviews a varied range of state-of-the-art models, algorithms, and procedures for image mining; emphasizes how to deal with real image data for practical image mining; highlights how such features as color, texture, and shape can be mined or extracted from images for image representation; presents four powerful approaches for classifying image data, namely, Bayesian classification, Support Vector Machines, Neural Networks, and Decision Trees; discusses techniques for indexing, image ranking, and image presentation, along with image database visualization methods; provides self-test exercises with instructions or Matlab code, as well as review summaries at the end of each chapter.This easy-to-follow work illuminates how concepts from fundamental and advanced mathematics can be applied to solve a broad range of image data mining problems encountered by students and researchers of computer science. Students of mathematics and other scientific disciplines will also benefit from the applications and solutions described in the text, together with the hands-on exercises that enable the reader to gain first-hand experience of computing. This reader-friendly textbook presents a comprehensive review of the essentials of image data mining, and the latest cutting-edge techniques used in the field. discusses techniques for indexing, image ranking, and image presentation, along with image database visualization methods; Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Publicado por Springer Nature Switzerland AG, CH, 2022
ISBN 10: 3030692531 ISBN 13: 9783030692537
Idioma: Inglés
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EUR 68,71
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Añadir al carritoPaperback. Condición: New. Second Edition 2021. This unique and useful textbook presents a comprehensive review of the essentials of image data mining, and the latest cutting-edge techniques used in the field. The coverage spans all aspects of image analysis and understanding, offering deep insights into areas of feature extraction, machine learning, and image retrieval. The theoretical coverage is supported by practical mathematical models and algorithms, utilizing data from real-world examples and experiments. Topics and features: Describes essential tools for image mining, covering Fourier transforms, Gabor filters, and contemporary wavelet transformsDevelops many new exercises (most with MATLAB code and instructions)Includes review summaries at the end of each chapterAnalyses state-of-the-art models, algorithms, and procedures for image miningIntegrates new sections on pre-processing, discrete cosine transform, and statistical inference and testingDemonstrates how features like color, texture, and shape can be mined or extracted for image representationApplies powerful classification approaches: Bayesian classification, support vector machines, neural networks, and decision treesImplements imaging techniques for indexing, ranking, and presentation, as well as database visualization This easy-to-follow, award-winning book illuminates how concepts from fundamental and advanced mathematics can be applied to solve a broad range of image data mining problems encountered by students and researchers of computer science. Students of mathematics and other scientific disciplines will also benefit from the applications and solutions described in the text, together with the hands-on exercises that enable the reader to gain first-hand experience of computing.
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Publicado por Springer Nature Switzerland AG, Cham, 2021
ISBN 10: 3030692507 ISBN 13: 9783030692506
Idioma: Inglés
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Añadir al carritoHardcover. Condición: new. Hardcover. This unique and useful textbook presents a comprehensive review of the essentials of image data mining, and the latest cutting-edge techniques used in the field. The coverage spans all aspects of image analysis and understanding, offering deep insights into areas of feature extraction, machine learning, and image retrieval. The theoretical coverage is supported by practical mathematical models and algorithms, utilizing data from real-world examples and experiments. Topics and features: Describes essential tools for image mining, covering Fourier transforms, Gabor filters, and contemporary wavelet transformsDevelops many new exercises (most with MATLAB code and instructions)Includes review summaries at the end of each chapterAnalyses state-of-the-art models, algorithms, and procedures for image miningIntegrates new sections on pre-processing, discrete cosine transform, and statistical inference and testingDemonstrates how features like color, texture, and shape can be mined or extracted for image representationApplies powerful classification approaches: Bayesian classification, support vector machines, neural networks, and decision treesImplements imaging techniques for indexing, ranking, and presentation, as well as database visualization This easy-to-follow, award-winning book illuminates how concepts from fundamental and advanced mathematics can be applied to solve a broad range of image data mining problems encountered by students and researchers of computer science. Students of mathematics and other scientific disciplines will also benefit from the applications and solutions described in the text, together with the hands-on exercises that enable the reader to gain first-hand experience of computing. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Librería: Biblios, Frankfurt am main, HESSE, Alemania
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Publicado por Springer-Nature New York Inc, 2020
ISBN 10: 3030179915 ISBN 13: 9783030179915
Idioma: Inglés
Librería: Revaluation Books, Exeter, Reino Unido
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Añadir al carritoPaperback. Condición: Brand New. 348 pages. 9.25x6.10x0.82 inches. In Stock.
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