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Digital Modeling of Material Appearance
Computer graphics systems are capable of generating stunningly realistic images of objects that have never physically existed. In order for computers to create these accurately detailed images, digital models of appearance must include robust data to give viewers a credible visual impression of the depicted materials. In particular, digital models demonstrating the nuances of how materials interact with light are essential to this capability. Digital Modeling of Material Appearance is the first comprehensive work on the digital modeling of material appearance: it explains how models from physics and engineering are combined with keen observation skills for use in computer graphics rendering....
Computational Texture and Patterns
Visual pattern analysis is a fundamental tool in mining data for knowledge. Computational representations for patterns and texture allow us to summarize, store, compare, and label in order to learn about the physical world. Our ability to capture visual imagery with cameras and sensors has resulted in vast amounts of raw data, but using this information effectively in a task-specific manner requires sophisticated computational representations. We enumerate specific desirable traits for these representations: (1) intraclass invariance—to support recognition; (2) illumination and geometric invariance for robustness to imaging conditions; (3) support for prediction and synthesis to use the mo...
A Computational Approach to Digital Chinese Painting and Calligraphy
"A Computational Approach to Digital Chinese Painting and Calligraphy" is a technical book on computer science and its applications in the arts. It focuses on Oriental digital arts, in particular Chinese arts and painting, offering a multi-disciplinary treatment from the angles of computer graphics, interactive techniques, human-computer interaction, and artificial intelligence. The book also discusses the unique difficulties and challenges of using the computer to produce Oriental arts, including research results by the authors and their lessons and engineering experiences behind these efforts. Songhua Xu is a computer scientist of Zhejiang University and Yale University, as well as an honorary researcher of the University of Hong Kong. Francis C.M. Lau is Professor at the University of Hong Kong where he leads the Systems Research Group in the Department of Computer Science. Yunhe Pan is Professor of Computer Science at Zhejiang University as well as Deputy President of Chinese Academy of Engineering.
The Magic of Computer Graphics
Computer graphics is a vast field that is becoming larger every day. It is impossible to cover every topic of interest, even within a specialization such as CG rendering. For many years, Noriko Kurachi has reported on the latest developments for Japanese readers in her monthly column for CG World. Being something of a pioneer herself, she selected topics that represented original and promising new directions for research. Many of these novel ideas are the topics covered in The Magic of Computer Graphics. Starting from the basic behavior of light, the first section of the book introduces the most useful techniques for global and local illumination using geometric descriptions of an environment. The second section goes on to describe image-based techniques that rely on captured data to do their magic. In the final section, the author looks at the synthesis of these two complementary approaches and what they mean for the future of computer graphics.
Multi-Modal Face Presentation Attack Detection
For the last ten years, face biometric research has been intensively studied by the computer vision community. Face recognition systems have been used in mobile, banking, and surveillance systems. For face recognition systems, face spoofing attack detection is a crucial stage that could cause severe security issues in government sectors. Although effective methods for face presentation attack detection have been proposed so far, the problem is still unsolved due to the difficulty in the design of features and methods that can work for new spoofing attacks. In addition, existing datasets for studying the problem are relatively small which hinders the progress in this relevant domain. In order...
Computer Vision in the Infrared Spectrum
Human visual perception is limited to the visual-optical spectrum. Machine vision is not. Cameras sensitive to the different infrared spectra can enhance the abilities of autonomous systems and visually perceive the environment in a holistic way. Relevant scene content can be made visible especially in situations, where sensors of other modalities face issues like a visual-optical camera that needs a source of illumination. As a consequence, not only human mistakes can be avoided by increasing the level of automation, but also machine-induced errors can be reduced that, for example, could make a self-driving car crash into a pedestrian under difficult illumination conditions. Furthermore, mu...
Visual Domain Adaptation in the Deep Learning Era
Solving problems with deep neural networks typically relies on massive amounts of labeled training data to achieve high performance. While in many situations huge volumes of unlabeled data can be and often are generated and available, the cost of acquiring data labels remains high. Transfer learning (TL), and in particular domain adaptation (DA), has emerged as an effective solution to overcome the burden of annotation, exploiting the unlabeled data available from the target domain together with labeled data or pre-trained models from similar, yet different source domains. The aim of this book is to provide an overview of such DA/TL methods applied to computer vision, a field whose popularit...
Person Re-Identification with Limited Supervision
Person re-identification is the problem of associating observations of targets in different non-overlapping cameras. Most of the existing learning-based methods have resulted in improved performance on standard re-identification benchmarks, but at the cost of time-consuming and tediously labeled data. Motivated by this, learning person re-identification models with limited to no supervision has drawn a great deal of attention in recent years. In this book, we provide an overview of some of the literature in person re-identification, and then move on to focus on some specific problems in the context of person re-identification with limited supervision in multi-camera environments. We expect t...
Advances in Visual Computing
It is with greatpleasure that we present the proceedings of the 5th International Symposium on Visual Computing (ISVC 2009), which was held in Las Vegas, Nevada. ISVC o?ers a common umbrella for the four main areas of visual c- puting includingvision,graphics,visualization,andvirtualreality.Thegoalisto provide a forum for researchers, scientists, engineers, and practitioners throu- out the world to present their latest research ?ndings, ideas, developments, and applications in the broader area of visual computing. This year, the program consisted of 16 oral sessions, one poster session, 7 special tracks, and 6 keynote presentations. Also, this year ISVC hosted the Third Semantic Robot Vision...
Handbook of Texture Analysis
Texture analysis is one of the fundamental aspects of human vision by which we discriminate between surfaces and objects. In a similar manner, computer vision can take advantage of the cues provided by surface texture to distinguish and recognize objects. In computer vision, texture analysis may be used alone or in combination with other sensed features (e.g. color, shape, or motion) to perform the task of recognition. Either way, it is a feature of paramount importance and boasts a tremendous body of work in terms of both research and applications.Currently, the main approaches to texture analysis must be sought out through a variety of research papers. This collection of chapters brings together in one handy volume the major topics of importance, and categorizes the various techniques into comprehensible concepts. The methods covered will not only be relevant to those working in computer vision, but will also be of benefit to the computer graphics, psychophysics, and pattern recognition communities, academic or industrial.
