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Publicado por LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203583561 ISBN 13: 9786203583564
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ISBN 10: 6203583561 ISBN 13: 9786203583564
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Añadir al carritoTaschenbuch. Condición: Neu. Deep Learning Based Emotion Recognition for Image and Video Signals | Matlab Implementation | Arselan Ashraf (u. a.) | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786203583564 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Publicado por Elsevier - Health Sciences Division, 2025
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Añadir al carritoCondición: New. Provides step-by-step guidance on implementing deep learning techniques, specifically for video and image processing tasks in real-world scenariosEmphasizes lightweight and efficient approaches to deep learning, ensuring that readers learn .
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Publicado por Elsevier - Health Sciences Division Jun 2025, 2025
ISBN 10: 044330078X ISBN 13: 9780443300783
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Añadir al carritoTaschenbuch. Condición: Neu. Neuware - Artificial intelligence technology has entered an extraordinary phase of fast development and wide application. The techniques developed in traditional AI research areas, such as computer vision and object recognition, have found many innovative applications in an array of real-world settings. The general methodological contributions from AI, such as a variety of recently developed deep learning algorithms, have also been applied to a wide spectrum of fields such as surveillance applications, real-time processing, IoT devices, and health care systems. The state-of-the-art and deep learning models have wider applicability and are highly efficient. Deep Learning in Action: Image and Video Processing for Practical Use provides a comprehensive and accessible resource for both intermediate to advanced readers seeking to harness the power of deep learning in the domains of video and image processing. The book bridges the gap between theoretical concepts and practical implementation by emphasizing lightweight approaches, enabling readers to efficiently apply deep learning techniques to real-world scenarios. It focuses on resource-efficient methods, making it particularly relevant in contexts where computational constraints are a concern.
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Publicado por LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203583561 ISBN 13: 9786203583564
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Publicado por LAP LAMBERT Academic Publishing Apr 2021, 2021
ISBN 10: 6203583561 ISBN 13: 9786203583564
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Emotion recognition utilizing pictures, videos, or speech as input is considered an intriguing issue in the research field over certain years. The introduction of deep learning procedures like the Convolutional Neural Networks (CNN) has made emotion recognition achieve promising outcomes. This book is carried out to develop an image and video-based emotion recognition model using CNN for automatic feature extraction and classification with Matlab sample codes. Five emotions are considered for recognition: angry, happy, neutral, sad, and surprise, compared to previous algorithms. Different pre-processing steps have been carried over data samples, followed by the popular and efficient Viola-Jones algorithm for face detection. Evaluating results using confusion matrix, accuracy, F1-score, precision, and recall shows that video-based datasets obtained more promising results than image-based datasets. 124 pp. Englisch.
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Publicado por LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203583561 ISBN 13: 9786203583564
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Publicado por LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203583561 ISBN 13: 9786203583564
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Añadir al carritoCondición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ashraf ArselanArselan Arshaf obtained his MSc degree from IIUM in 2021. Teddy Surya Gunawan received his PhD degree from UNSW in 2007 and is currently Professor at KOE, IIUM. Mira Kartiwi obtained her PhD from UOW in 2009 and is curr.
Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing Apr 2021, 2021
ISBN 10: 6203583561 ISBN 13: 9786203583564
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Emotion recognition utilizing pictures, videos, or speech as input is considered an intriguing issue in the research field over certain years. The introduction of deep learning procedures like the Convolutional Neural Networks (CNN) has made emotion recognition achieve promising outcomes. This book is carried out to develop an image and video-based emotion recognition model using CNN for automatic feature extraction and classification with Matlab sample codes. Five emotions are considered for recognition: angry, happy, neutral, sad, and surprise, compared to previous algorithms. Different pre-processing steps have been carried over data samples, followed by the popular and efficient Viola-Jones algorithm for face detection. Evaluating results using confusion matrix, accuracy, F1-score, precision, and recall shows that video-based datasets obtained more promising results than image-based datasets.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 124 pp. Englisch.
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Publicado por LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203583561 ISBN 13: 9786203583564
Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Emotion recognition utilizing pictures, videos, or speech as input is considered an intriguing issue in the research field over certain years. The introduction of deep learning procedures like the Convolutional Neural Networks (CNN) has made emotion recognition achieve promising outcomes. This book is carried out to develop an image and video-based emotion recognition model using CNN for automatic feature extraction and classification with Matlab sample codes. Five emotions are considered for recognition: angry, happy, neutral, sad, and surprise, compared to previous algorithms. Different pre-processing steps have been carried over data samples, followed by the popular and efficient Viola-Jones algorithm for face detection. Evaluating results using confusion matrix, accuracy, F1-score, precision, and recall shows that video-based datasets obtained more promising results than image-based datasets.
Librería: Biblios, Frankfurt am main, HESSE, Alemania
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