Items related to An Introduction to Image Classification: From Designed...
- PublisherSpringer
- Publication date2024
- ISBN 10 9819978815
- ISBN 13 9789819978816
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages306
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An Introduction to Image Classification
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An Introduction to Image Classification
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An Introduction to Image Classification
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An Introduction to Image Classification: From Designed Models to End-to-End Learning
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An Introduction to Image Classification: From Designed Models to End-to-End Learning
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An Introduction to Image Classification: From Designed Models to End-to-End Learning
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An Introduction to Image Classification: From Designed Models to End-to-End Learning
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An Introduction to Image Classification: From Designed Models to End-to-End Learning
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An Introduction to Image Classification
Published by
Springer Nature Singapore Feb 2024, 2024
ISBN 10: 9819978815
ISBN 13: 9789819978816
New
Hardcover
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - 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. 308 pp. Englisch. Seller Inventory # 9789819978816
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An Introduction to Image Classification : From Designed Models to End-to-End Learning
Published by
Springer Nature Singapore, Springer Nature Singapore, 2024
ISBN 10: 9819978815
ISBN 13: 9789819978816
New
Hardcover
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Buch. Condition: 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. Seller Inventory # 9789819978816
Quantity: 1 available