Abdussalam Elhanashi – författare

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.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 techniques that are suited to resource-constrained environmentsCovers a wide range of real-world applications, such as object detection, image segmentation, video classificationOffers a comprehensive understanding of how deep learning can be leveraged across various domainsEncourages hands-on experience that can be applied to the concepts to existing projects

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.- Provides step-by-step guidance on implementing deep learning techniques, specifically for video and image processing tasks in real-world scenarios- Emphasizes lightweight and efficient approaches to deep learning, ensuring that readers learn techniques that are suited to resource-constrained environments- Covers a wide range of real-world applications, such as object detection, image segmentation, video classification- Offers a comprehensive understanding of how deep learning can be leveraged across various domains- Encourages hands-on experience that can be applied to the concepts to existing projects

In the ever-evolving field of computer vision, "Deep Learning for Object Detection and Localization" serves as an indispensable resource for researchers, practitioners, and students alike. This comprehensive book delves into the latest advancements and methodologies in deep learning, specifically tailored to enhance object detection and localization tasks. From foundational concepts to cutting-edge techniques, readers will embark on a journey through the intricacies of convolutional neural networks (CNNs), region-based frameworks, and advanced algorithms that power modern object detection systems. The purpose of writing this book is to bridge the knowledge gap in the dynamic field of object detection and localization using deep learning. As technology progresses, there is an increasing demand for robust and efficient systems capable of identifying and pinpointing objects within images and videos. Despite the plethora of resources available, there remains a need for a focused, in-depth guide that comprehensively covers both theoretical aspects and practical implementations. This book aims to fulfill that need by providing a detailed, structured approach to mastering the complexities of object detection and localization.Readers will benefit from the problem-solving focus of this book, which addresses key challenges faced in real-world applications. Whether it's enhancing accuracy in autonomous driving, improving precision in medical imaging, or optimizing performance in surveillance systems, the book offers practical solutions and insights. By exploring state-of-the-art techniques and real-world case studies, "Deep Learning for Object Detection and Localization" equips readers with the knowledge and tools necessary to tackle the pressing challenges in this rapidly advancing field.