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Condition: New. 1st edition NO-PA16APR2015-KAP.
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Image Fusion in Remote Sensing: Conventional and Deep Learning Approaches (Synthesis Lectures on Image, Video, and Multimedia Processing)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Deep Learning Based Emotion Recognition for Image and Video Signals
Language: English
Published by LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203583561 ISBN 13: 9786203583564
Condition: New.
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paperback. Condition: Very Good. Publisher overstock. May have remainder mark / minor shelfwear. 99% of orders arrive in 4-10 days. Discounted shipping on multiple books.
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Deep Learning Based Emotion Recognition for Image and Video Signals | Matlab Implementation
Language: English
Published by LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203583561 ISBN 13: 9786203583564
Taschenbuch. Condition: 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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Condition: New. 1st edition NO-PA16APR2015-KAP.
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Deep Learning in Action: Image and Video Processing for Practical Use
Language: English
Published by Elsevier - Health Sciences Division, 2025
ISBN 10: 044330078X ISBN 13: 9780443300783
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Deep Learning in Action: Image and Video Processing for Practical Use
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Deep Learning in Action: Image and Video Processing for Practical Use
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Deep Learning in Action: Image and Video Processing for Practical Use
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Add to basketCondition: 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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Deep Learning in Action: Image and Video Processing for Practical Use
Language: English
Published by Elsevier - Health Sciences Division Jun 2025, 2025
ISBN 10: 044330078X ISBN 13: 9780443300783
Taschenbuch. Condition: 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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Deep Learning Based Emotion Recognition for Image and Video Signals
Language: English
Published by LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203583561 ISBN 13: 9786203583564
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Deep Learning Based Emotion Recognition for Image and Video Signals
Language: English
Published by LAP LAMBERT Academic Publishing Apr 2021, 2021
ISBN 10: 6203583561 ISBN 13: 9786203583564
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: 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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Deep Learning in Video and Image Processing
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Deep Learning Based Emotion Recognition for Image and Video Signals
Language: English
Published by LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203583561 ISBN 13: 9786203583564
Condition: New. PRINT ON DEMAND.
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Deep Learning Based Emotion Recognition for Image and Video Signals
Language: English
Published by LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203583561 ISBN 13: 9786203583564
Condition: 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.
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Deep Learning Based Emotion Recognition for Image and Video Signals
Language: English
Published by LAP LAMBERT Academic Publishing Apr 2021, 2021
ISBN 10: 6203583561 ISBN 13: 9786203583564
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: 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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Deep Learning Based Emotion Recognition for Image and Video Signals : Matlab Implementation
Language: English
Published by LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203583561 ISBN 13: 9786203583564
Taschenbuch. Condition: 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.
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Condition: New. Print on Demand.
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Condition: New. PRINT ON DEMAND.
