Bøger i Advances in Computer Vision and Pattern Recognition serien

This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases.

This book focuses on the fundamentals and recent advances in RGB-D imaging as well as covering a range of RGB-D applications. The addition of depth data to regular RGB images vastly increases the range of applications, and has resulted in a demand for robust and real-time processing of RGB-D data.

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature.

This volume provides students, researchers and application developers with the knowledge and tools to get the most out of using neural networks and related data modelling techniques to solve pattern recognition problems. Each chapter covers a group of related pattern recognition techniques and includes a range of examples to show how these techniques can be applied to solve practical problems. Features of particular interest include:- A NETLAB toolbox which is freely available- Worked examples, demonstration programs and over 100 graded exercises- Cutting edge research made accessible for the first time in a highly usable form- Comprehensive coverage of visualisation methods, Bayesian techniques for neural networks and Gaussian ProcessesAlthough primarily a textbook for teaching undergraduate and postgraduate courses in pattern recognition and neural networks, this book will also be of interest to practitioners and researchers who can use the toolbox to develop application solutions and new models."...provides a unique collection of many of the most important pattern recognition algorithms. With its use of compact and easily modified MATLAB scripts, the book is ideally suited to both teaching and research."Christopher Bishop, Microsoft Research, Cambridge, UK"...a welcome addition to the literature on neural networks and how to train and use them to solve many of the statistical problems that occur in data analysis and data mining" Jack Cowan, Mathematics Department, University of Chicago, US"If you have a pattern recognition problem, you should consider NETLAB; if you use NETLAB you must have this book." Keith Worden, University of Sheffield, UK

Visual Question Answering (VQA) usually combines visual inputs like image and video with a natural language question concerning the input and generates a natural language answer as the output. This is by nature a multi-disciplinary research problem, involving computer vision (CV), natural language processing (NLP), knowledge representation and reasoning (KR), etc.Further, VQA is an ambitious undertaking, as it must overcome the challenges of general image understanding and the question-answering task, as well as the difficulties entailed by using large-scale databases with mixed-quality inputs. However, with the advent of deep learning (DL) and driven by the existence of advanced techniques in both CV and NLP and the availability of relevant large-scale datasets, we have recently seen enormous strides in VQA, with more systems and promising results emerging.This book provides a comprehensive overview of VQA, covering fundamental theories, models, datasets, and promising future directions. Given its scope, it can be used as a textbook on computer vision and natural language processing, especially for researchers and students in the area of visual question answering. It also highlights the key models used in VQA.

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature.

This book describes active illumination techniques in computer vision. The final part shows how such active illumination techniques can be applied to various domains, describing the issue to be overcome by active illumination techniques and the advantages of using these techniques.

Discover the captivating world of computer vision and deep learning for autonomous driving with our comprehensive and in-depth guide. Immerse yourself in an in-depth exploration of cutting-edge topics, carefully crafted to engage tertiary students and ignite the curiosity of researchers and professionals in the field. From fundamental principles to practical applications, this comprehensive guide offers a gentle introduction, expert evaluations of state-of-the-art methods, and inspiring research directions. With a broad range of topics covered, it is also an invaluable resource for university programs offering computer vision and deep learning courses. This book provides clear and simplified algorithm descriptions, making it easy for beginners to understand the complex concepts. We also include carefully selected problems and examples to help reinforce your learning. Don't miss out on this essential guide to computer vision and deep learning for autonomous driving.