Computer Vision For Human Machine Interaction

Leading scientists describe how advances in computer vision can change how we interact with computers.

The need for natural and effective Human-Computer Interaction (HCI) is increasingly important due to the prevalence of computers in human activities. Computer vision and pattern recognition continue to play a dominant role in the HCI realm. However, computer vision methods often fail to become pervasive in the field due to the lack of real-time, robust algorithms, and novel and convincing applications. This state-of-the-art contributed volume is comprised of articles by prominent experts in computer vision, pattern recognition and HCI. It is the first published text to capture the latest research in this rapidly advancing field with exclusive focus on real-time algorithms and practical applications in diverse and numerous industries, and it outlines further challenges in these areas. Real-Time Vision for Human-Computer Interaction is an invaluable reference for HCI researchers in both academia and industry, and a useful supplement for advanced-level courses in HCI and Computer Vision.

This book constitutes the refereed proceedings of the International Workshop on Human-Computer Interaction, HCI 2004, held at ECCV 2004 in Prague, Czech Republic in May 2004. The 19 revised full papers presented together with an introductory overview and an invited paper were carefully reviewed and selected from 45 submissions. The papers are organized in topical sections on human-robot interaction, gesture recognition and body tracking, systems, and face and head.

Human-Computer Interaction (HCI) lies at the crossroads of many scienti?c areas including arti?cial intelligence, computer vision, face recognition, motion tracking, etc. In order for HCI systems to interact seamlessly with people, they need to understand their environment through vision and auditory input. Mo- over, HCI systems should learn how to adaptively respond depending on the situation. The goal of this workshop was to bring together researchers from the ?eld of computer vision whose work is related to human-computer interaction. The selected articles for this workshop address a wide range of theoretical and - plication issues in human-computer interaction ranging from human-robot - teraction, gesture recognition, and body tracking, to facial features analysis and human-computer interaction systems. This year 74 papers from 18 countries were submitted and 22 were accepted for presentation at the workshop after being reviewed by at least 3 members of the Program Committee. We had therefore a very competitive acceptance rate of less than 30% and as a consequence we had a very-high-quality workshop. Wewouldliketo thankallmembersofthe ProgramCommitteefor their help in ensuring the quality of the papers accepted for publication. We are grateful to Dr. Jian Wang for giving the keynote address. In addition, we wish to thank the organizers of the 10th IEEE International Conference on Computer Vision and our sponsors, University of Amsterdam, Leiden Institute of Advanced Computer Science, and the University of Illinois at Urbana-Champaign, for support in setting up our workshop.

Contemporary man-machine interfaces are increasingly characterized by multimodality, nonintrusiveness, context-sensitivity, adaptivity, and teleoperability. The implementation of such properties relies on novel techniques in felds such as, e.g., computer vision, speech technology, trainable classifiers, robotics, and virtual reality. This book puts special emphasis on technological aspects of advanced interface implementation. Furthermore it focuses on interface design and usability. For readers with a background in engineering and computer science, most chapters offer design guidelines and case studies, as well as a description of the functioning and limitations of the algorithms required for implementation. In addition, complementary code examples in C++ are given where appropriate. As a special feature the book is accompanied by two easy-to-handle software development environments, which offer access to extensive public domain software for computer vision, classification, and virtual reality. These environments also provide real-time access to peripheral components like, e.g., webcams or microphones, enabling hands-on experimentation and testing.

Natural interaction is one of the hottest research issues in human-computer interaction. At present, there is an urgent need for intelligent devices (service robots, virtual humans, etc.) to be able to understand intentions in an interactive dialogue. Focusing on human-computer understanding based on deep learning methods, the book systematically introduces readers to intention recognition, unknown intention detection, and new intention discovery in human-computer dialogue. This book is the first to present interactive dialogue intention analysis in the context of natural interaction. In addition to helping readers master the key technologies and concepts of human-machine dialogue intention analysis and catch up on the latest advances, it includes valuable references for further research.

Human Machine Interaction, or more commonly Human Computer Interaction, is the study of interaction between people and computers. It is an interdisciplinary field, connecting computer science with many other disciplines such as psychology, sociology and the arts. The present volume documents the results of the MMI research program on Human Machine Interaction involving 8 projects (selected from a total of 80 proposals) funded by the Hasler Foundation between 2005 and 2008. These projects were also partially funded by the associated universities and other third parties such as the Swiss National Science Foundation. This state-of-the-art survey begins with three chapters giving overviews of the domains of multimodal user interfaces, interactive visualization, and mixed reality. These are followed by eight chapters presenting the results of the projects, grouped according to the three aforementioned themes.

This book provides a unique view of human activity recognition, especially fine-grained human activity structure learning, human-interaction recognition, RGB-D data based action recognition, temporal decomposition, and causality learning in unconstrained human activity videos. The techniques discussed give readers tools that provide a significant improvement over existing methodologies of video content understanding by taking advantage of activity recognition. It links multiple popular research fields in computer vision, machine learning, human-centered computing, human-computer interaction, image classification, and pattern recognition. In addition, the book includes several key chapters covering multiple emerging topics in the field. Contributed by top experts and practitioners, the chapters present key topics from different angles and blend both methodology and application, composing a solid overview of the human activity recognition techniques.