Isbn: 9789819833344 - deep learning methods for image object detection and recognition (9 risultati)

Condizione: New.

Hardback. Condizione: New. This book provides a comprehensive introduction on applying deep learning to the visual recognition of objects, bridging the gap between theoretical algorithms and high-impact practical implementations. Within these pages, readers will find a detailed exploration of advanced techniques for image restoration-including UNet-based defogging, feature fusion GANs, and ESRGAN super-resolution-alongside high-performance detection models. Specialized applications such as underwater crack segmentation using transfer learning, marine biological detection, and real-time analysis through YOLOv4, RetinaNet, and LSTM-integrated networks are also covered to provide researchers and engineers with a technical blueprint for solving diverse real-world computer vision challenges. Additionally, this work is also suitable as a textbook for advanced undergraduate and graduate students majoring in Artificial Intelligence, Intelligent Science and Technology, Computer Science, and Automation.…

Hardcover. Condizione: Brand New. 258 pages. 6.24x0.80x9.24 inches. In Stock.

Hardback. Condizione: New. This book provides a comprehensive introduction on applying deep learning to the visual recognition of objects, bridging the gap between theoretical algorithms and high-impact practical implementations. Within these pages, readers will find a detailed exploration of advanced techniques for image restoration-including UNet-based defogging, feature fusion GANs, and ESRGAN super-resolution-alongside high-performance detection models. Specialized applications such as underwater crack segmentation using transfer learning, marine biological detection, and real-time analysis through YOLOv4, RetinaNet, and LSTM-integrated networks are also covered to provide researchers and engineers with a technical blueprint for solving diverse real-world computer vision challenges. Additionally, this work is also suitable as a textbook for advanced undergraduate and graduate students majoring in Artificial Intelligence, Intelligent Science and Technology, Computer Science, and Automation.…

Hardcover. Condizione: new. Hardcover. This book provides a comprehensive introduction on applying deep learning to the visual recognition of objects, bridging the gap between theoretical algorithms and high-impact practical implementations. Within these pages, readers will find a detailed exploration of advanced techniques for image restorationincluding UNet-based defogging, feature fusion GANs, and ESRGAN super-resolutionalongside high-performance detection models. Specialized applications such as underwater crack segmentation using transfer learning, marine biological detection, and real-time analysis through YOLOv4, RetinaNet, and LSTM-integrated networks are also covered to provide researchers and engineers with a technical blueprint for solving diverse real-world computer vision challenges. Additionally, this work is also suitable as a textbook for advanced undergraduate and graduate students majoring in Artificial Intelligence, Intelligent Science and Technology, Computer Science, and Automation. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Hardcover. Condizione: new. Hardcover. This book provides a comprehensive introduction on applying deep learning to the visual recognition of objects, bridging the gap between theoretical algorithms and high-impact practical implementations. Within these pages, readers will find a detailed exploration of advanced techniques for image restorationincluding UNet-based defogging, feature fusion GANs, and ESRGAN super-resolutionalongside high-performance detection models. Specialized applications such as underwater crack segmentation using transfer learning, marine biological detection, and real-time analysis through YOLOv4, RetinaNet, and LSTM-integrated networks are also covered to provide researchers and engineers with a technical blueprint for solving diverse real-world computer vision challenges. Additionally, this work is also suitable as a textbook for advanced undergraduate and graduate students majoring in Artificial Intelligence, Intelligent Science and Technology, Computer Science, and Automation. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Hardcover. Condizione: new. Hardcover. This book provides a comprehensive introduction on applying deep learning to the visual recognition of objects, bridging the gap between theoretical algorithms and high-impact practical implementations. Within these pages, readers will find a detailed exploration of advanced techniques for image restorationincluding UNet-based defogging, feature fusion GANs, and ESRGAN super-resolutionalongside high-performance detection models. Specialized applications such as underwater crack segmentation using transfer learning, marine biological detection, and real-time analysis through YOLOv4, RetinaNet, and LSTM-integrated networks are also covered to provide researchers and engineers with a technical blueprint for solving diverse real-world computer vision challenges. Additionally, this work is also suitable as a textbook for advanced undergraduate and graduate students majoring in Artificial Intelligence, Intelligent Science and Technology, Computer Science, and Automation. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Buch. Condizione: Neu. DEEP LEARNING METHODS IMAGE OBJECT DETECTION & RECOGNITION | Shi Pengfei | Buch | Englisch | 2026 | World Scientific | EAN 9789819833344 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book provides a comprehensive introduction on applying deep learning to the visual recognition of objects, bridging the gap between theoretical algorithms and high-impact practical implementations. Within these pages, readers will find a detailed exploration of advanced techniques for image restoration-including UNet-based defogging, feature fusion GANs, and ESRGAN super-resolution-alongside high-performance detection models. Specialized applications such as underwater crack segmentation using transfer learning, marine biological detection, and real-time analysis through YOLOv4, RetinaNet, and LSTM-integrated networks are also covered to provide researchers and engineers with a technical blueprint for solving diverse real-world computer vision challenges. Additionally, this work is also suitable as a textbook for advanced undergraduate and graduate students majoring in Artificial Intelligence, Intelligent Science and Technology, Computer Science, and Automation.…