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Zustand: Bueno. : Este libro es una colección de estudios del sociólogo alemán Ferdinand Tönnies sobre los conceptos de comunidad y sociedad. Publicado por VS Verlag für Sozialwissenschaften en 2012, forma parte de la serie 'Klassiker der Sozialwissenschaften' (Clásicos de las Ciencias Sociales). Los estudios documentan el desarrollo intelectual de Tönnies, desde su primer borrador de 'Gemeinschaft und Gesellschaft' en los años 1880-1881 hasta su contribución homónima en el 'Handwörterbuch der Soziologie' (Diccionario de Sociología) editado por Alfred Vierkandt en 1931. Esta colección destaca la relevancia continua de su trabajo en el campo de la sociología. EAN: 9783531162409 Tipo: Libros Categoría: Otros Título: Studien zu Gemeinschaft und Gesellschaft Autor: Ferdinand Tönnies Editorial: VS Verlag für Sozialwissenschaften Idioma: de-DE Páginas: 284 Formato: tapa blanda.

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Zustand: As New. Unread book in perfect condition.

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Zustand: New. pp. 490.

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Taschenbuch. Zustand: Neu. Neuware -Image classification is a critical component in computer vision tasks and has numerous applications. Traditional methods for image classification involve feature extraction and classification in feature space. Current state-of-the-art methods utilize end-to-end learning with deep neural networks, where feature extraction and classification are integrated into the model. Understanding traditional image classification is important because many of its design concepts directly correspond to components of a neural network. This knowledge can help demystify the behavior of these networks, which may seem opaque at first sight.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 308 pp. Englisch.

Buch. Zustand: Neu. Neuware -This comprehensive guide provides a uniquely practical, application-focused introduction to medical image analysis. This fully updated new edition has been enhanced with material on the latest developments in the field, whilst retaining the original focus on segmentation, classification and registration. Topics and features: presents learning objectives, exercises and concluding remarks in each chapter; describes a range of common imaging techniques, reconstruction techniques and image artifacts, and discusses the archival and transfer of images; reviews an expanded selection of techniques for image enhancement, feature detection, feature generation, segmentation, registration, and validation; examines analysis methods in view of image-based guidance in the operating room (NEW); discusses the use of deep convolutional networks for segmentation and labeling tasks (NEW); includes appendices on Markov random field optimization, variational calculus and principal component analysis. 589 pp. Englisch.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Image classification is a critical component in computer vision tasks and has numerous applications. Traditional methods for image classification involve feature extraction and classification in feature space. Current state-of-the-art methods utilize end-to-end learning with deep neural networks, where feature extraction and classification are integrated into the model. Understanding traditional image classification is important because many of its design concepts directly correspond to components of a neural network. This knowledge can help demystify the behavior of these networks, which may seem opaque at first sight.The book starts from introducing methods for model-driven feature extraction and classification, including basic computer vision techniques for extracting high-level semantics from images. A brief overview of probabilistic classification with generative and discriminative classifiers is then provided. Next, neural networks are presented as a means to learn a classification model directly from labeled sample images, with individual components of the network discussed. The relationships between network components and those of a traditional designed model are explored, and different concepts for regularizing model training are explained. Finally, various methods for analyzing what a network has learned are covered in the closing section of the book.The topic of image classification is presented as a thoroughly curated sequence of steps that gradually increase understanding of the working of a fully trainable classifier. Practical exercises in Python/Keras/Tensorflow have been designed to allow for experimental exploration of these concepts. In each chapter, suitable functions from Python modules are briefly introduced to provide students with the necessary tools to conduct these experiments.

Taschenbuch. Zustand: Neu. Neuware -This comprehensive guide provides a uniquely practical, application-focused introduction to medical image analysis. This fully updated new edition has been enhanced with material on the latest developments in the field, whilst retaining the original focus on segmentation, classification and registration. Topics and features: presents learning objectives, exercises and concluding remarks in each chapter; describes a range of common imaging techniques, reconstruction techniques and image artifacts, and discusses the archival and transfer of images; reviews an expanded selection of techniques for image enhancement, feature detection, feature generation, segmentation, registration, and validation; examines analysis methods in view of image-based guidance in the operating room (NEW); discusses the use of deep convolutional networks for segmentation and labeling tasks (NEW); includes appendices on Markov random field optimization, variational calculus and principal component analysis.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 616 pp. Englisch.

Hardcover. Zustand: Brand New. 306 pages. 9.25x6.25x1.00 inches. In Stock.

Paperback. Zustand: Brand New. 488 pages. 9.00x6.00x1.25 inches. In Stock.

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Taschenbuch. Zustand: Neu. Neuware -This book presents a comprehensive overview of medical image analysis. Practical in approach, the text is uniquely structured by potential applications. Features: presents learning objectives, exercises and concluding remarks in each chapter, in addition to a glossary of abbreviations; describes a range of common imaging techniques, reconstruction techniques and image artefacts; discusses the archival and transfer of images, including the HL7 and DICOM standards; presents a selection of techniques for the enhancement of contrast and edges, for noise reduction and for edge-preserving smoothing; examines various feature detection and segmentation techniques, together with methods for computing a registration or normalisation transformation; explores object detection, as well as classification based on segment attributes such as shape and appearance; reviews the validation of an analysis method; includes appendices on Markov random field optimization, variational calculus and principal component analysis.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 488 pp. Englisch.

Buch. Zustand: Neu. Neuware -Image classification is a critical component in computer vision tasks and has numerous applications. Traditional methods for image classification involve feature extraction and classification in feature space. Current state-of-the-art methods utilize end-to-end learning with deep neural networks, where feature extraction and classification are integrated into the model. Understanding traditional image classification is important because many of its design concepts directly correspond to components of a neural network. This knowledge can help demystify the behavior of these networks, which may seem opaque at first sight.The book starts from introducing methods for model-driven feature extraction and classification, including basic computer vision techniques for extracting high-level semantics from images. A brief overview of probabilistic classification with generative and discriminative classifiers is then provided. Next, neural networks are presented as a means to learn a classification model directly from labeled sample images, with individual components of the network discussed. The relationships between network components and those of a traditional designed model are explored, and different concepts for regularizing model training are explained. Finally, various methods for analyzing what a network has learned are covered in the closing section of the book.The topic of image classification is presented as a thoroughly curated sequence of steps that gradually increase understanding of the working of a fully trainable classifier. Practical exercises in Python/Keras/Tensorflow have been designed to allow for experimental exploration of these concepts. In each chapter, suitable functions from Python modules are briefly introduced to provide students with the necessary tools to conduct these experiments.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 308 pp. Englisch.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This comprehensive guide provides a uniquely practical, application-focused introduction to medical image analysis. This fully updated new edition has been enhanced with material on the latest developments in the field, whilst retaining the original focus on segmentation, classification and registration. Topics and features: presents learning objectives, exercises and concluding remarks in each chapter; describes a range of common imaging techniques, reconstruction techniques and image artifacts, and discusses the archival and transfer of images; reviews an expanded selection of techniques for image enhancement, feature detection, feature generation, segmentation, registration, and validation; examines analysis methods in view of image-based guidance in the operating room (NEW); discusses the use of deep convolutional networks for segmentation and labeling tasks (NEW); includes appendices on Markov random field optimization, variational calculus and principal component analysis.