Bøker i Foundations and Trends (R) in Computer Graphics and Vision-serien

Broadly summarises the key aspects of generalized sampling, and delves into specific applications in graphics. Using new notation, this book concisely presents and extends several key techniques. In addition, it demonstrates benefits for prefiltering in image downscaling and supersample-based rendering.

Introduces the latest trends and deep-learning-based techniques for multimedia forensics in both architectural and data-processing. Different techniques used to manipulate content are presented, followed by image and video forgery techniques. Deep learning methods for source identification and solutions for deepfake detection are covered.

Explores discrete energy minimization for discrete graphical models. The book considers graphical models, or, more precisely, maximum a posteriori inference for graphical models, purely as a combinatorial optimization problem.

Provides a detailed guide to the mathematical theory and computer algorithms for line drawing of 3D objects. The book focuses on the curves known as contours as they are the most important curves for line drawing of 3D surfaces. The authors describe the different algorithms required to compute and render these curves.

Presents foundations, original research and trends in the field of object categorization by computer vision methods. The book provides a review of existing representations, algorithms, systems and databases for visual object categorization.

Presents a comprehensive survey of the currently available Web3D tools and their applications. The authors define a schema of the available possibilities and features supported by enabling technologies and implemented systems.

Provides an overview of how crowdsourcing has been used in computer vision, enabling a computer vision researcher who has previously not collected non-expert data to devise a data collection strategy.

Provides the reader with an overview of path tracing and highlights important milestones in its development that have led to it becoming the preferred movie rendering technique today. The book identifies major hurdles that stood in the way of that transition, describing the technical milestones that pushed the field forward.

Presents a hands-on view of the field of multi-view stereo with a focus on practical algorithms. Multi-view stereo algorithms are able to construct highly detailed 3D models from images alone. They take a possibly very large set of images and construct a 3D plausible geometry that explains the images under some reasonable assumptions.

Provides the reader with a self-contained view of sparse modeling for visual recognition and image processing. More specifically, the work focuses on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.

While several survey papers on particular sub-problems have appeared, no comprehensive survey on problems, datasets, and methods in computer vision for autonomous vehicles has been published. This monograph fills this gap by providing a survey on the state-of-the-art datasets and techniques.

Provides a summary of the relevant mathematical theory, a historic perspective of some important symmetry-related ideas, a partial report on the state of the arts symmetry detection algorithms along with its first quantitative benchmark, a diverse set of real world applications, suggestions for future directions, and a comprehensive reference list.

Many applications require tracking complex 3D objects. These include visual serving of robotic arms on specific target objects, Augmented Reality systems that require real time registration of the object to be augmented, and head tracking systems. This book reviews the techniques and approaches that have been developed by industry and research.

Computational visual perception seeks to reproduce human vision through the combination of visual sensors, artificial intelligence, and computing. This monograph focuses on the inference component of the problem and in particular discusses in a systematic manner the most commonly used optimization principles in the context of graphical models.

Reconstructing the shape of an object from images is an important problem in computer vision that has led to a variety of solution strategies. This monograph focuses on photometric stereo, that is, techniques that exploit the observed intensity variations caused by illumination changes to recover the orientation of the surface.

Provides a comprehensive overview of domain adaptation solutions for visual recognition problems. By starting with the problem description and illustrations, it discusses three adaptation scenarios - unsupervised adaptation, semi-supervised adaptation, and multi-domain heterogeneous adaptation.

Introduces the reader to the most popular classes of structured models in computer vision. The focus is on discrete undirected graphical models which are covered in detail together with a description of algorithms for both probabilistic inference and maximum a posteriori inference.

Image-based rendering (IBR) is unique in that it requires computer graphics, computer vision, and image processing to join forces to solve a common goal, namely photorealistic rendering through the use of images. Image-Based Rendering surveys the various techniques used in the area.

Describes what the authors consider are fundamental building blocks for geometric computer vision or structure-from-motion: epipolar geometry, pose and motion estimation, 3D scene modeling, and bundle adjustment. The main goal is to highlight the core principles of these, which are independent of specific camera models.

Discusses methods to extract 3-dimensional (3D) models from plain images. In particular, the 3D information is obtained from images for which the camera parameters are unknown. The principles underlying such uncalibrated structure-from-motion methods are outlined.