Zhengyou Zhang – författare

Human faces are familiar to our visual systems. We easily recognize a person's face in arbitrary lighting conditions and in a variety of poses; detect small appearance changes; and notice subtle expression details. Can computer vision systems process face images as well as human vision systems can? Face image processing has potential applications in surveillance, image and video search, social networking and other domains. A comprehensive guide to this fascinating topic, this book provides a systematic description of modeling face geometry and appearance from images, including information on mathematical tools, physical concepts, image processing and computer vision techniques, and concrete prototype systems. The book will be an excellent reference for researchers and graduate students in computer vision, computer graphics and multimedia, as well as application developers who would like to gain a better understanding of the state of the art.

This text deals with one of the oldest problems in computer vision, namely to recover the 3-D geometric and kinematic structures of the world from two images, and to recognise a 3-D object in a cluttered scene from one or several views of this object in a different setting. Several different problems have been unified through the use of a unique geometric concept. On the one hand, the geometrical point of view naturally leads to the consideration of the projective geometric structure which relates two images of the same object, the epipolar structure. On the other hand, the practitioner's point of view allows the reduction of the difficult 2-D search tasks encountered in the fundamental problems to much simpler 1-D search tasks, and, for the theoretically inclined reader, it ties the projective, affine and Euclidean structures of the scene to those of the images.Audience: The authors have managed to avoid projective geometry in their exposition, and to guide the reader through the various aspects of epipolar geometry, stereo vision, motion analysis and object recognition using only the standard tools of linear algebra, thus making this a valuable book for a wide audience of researchers and engineers in image-related fields like vision, image processing, computer graphics, robotics, multi-media, and virtual reality.

Table of Contents: A Brief Survey of the Face Detection Literature / Cascade-based Real-Time Face Detection / Multiple Instance Learning for Face Detection / Detector Adaptation / Other Applications / Conclusions and Future Work

examines the effective features that characterize static hand poses and introduces a unified framework to enforce both temporal and spatial constraints for hand parsing; proposes a new classifier architecture for real-time hand pose recognition and a novel hand segmentation and gesture recognition system.

This book presents an interdisciplinary selection of cutting-edge research on RGB-D based computer vision. Features: discusses the calibration of color and depth cameras, the reduction of noise on depth maps and methods for capturing human performance in 3D; reviews a selection of applications which use RGB-D information to reconstruct human figures, evaluate energy consumption and obtain accurate action classification; presents an approach for 3D object retrieval and for the reconstruction of gas flow from multiple Kinect cameras; describes an RGB-D computer vision system designed to assist the visually impaired and another for smart-environment sensing to assist elderly and disabled people; examines the effective features that characterize static hand poses and introduces a unified framework to enforce both temporal and spatial constraints for hand parsing; proposes a new classifier architecture for real-time hand pose recognition and a novel hand segmentation and gesture recognition system.

This volume treats the analysis of 3D dynamic scenes using a stereovision system. Several approaches are described, for example two different methods of dealing with long and short sequences of images of an unknown environment, including an arbitrary number of rigid mobile objects. Results obtained from stereovision systems are found to be superior to those from monocular image systems, which are often very sensitive to noise and therefore of little use in practice. It is shown that motion estimation can be further improved by the explicit modelling of uncertainty in geometric objects. The techniques developed in this book have been successfully demonstrated with a large number of real images in the context of visual navigation of a mobile robot.

he problem of analyzing sequences of images to extract three-dimensional T motion and structure has been at the heart of the research in computer vi­ sion for many years. It is very important since its success or failure will determine whether or not vision can be used as a sensory process in reactive systems. The considerable research interest in this field has been motivated at least by the following two points: 1. The redundancy of information contained in time-varying images can over­ come several difficulties encountered in interpreting a single image. 2. There are a lot of important applications including automatic vehicle driv­ ing, traffic control, aerial surveillance, medical inspection and global model construction. However, there are many new problems which should be solved: how to effi­ ciently process the abundant information contained in time-varying images, how to model the change between images, how to model the uncertainty inherently associated with the imaging system and how to solve inverse problems which are generally ill-posed. There are of course many possibilities for attacking these problems and many more remain to be explored. We discuss a few of them in this book based on work carried out during the last five years in the Computer Vision and Robotics Group at INRIA (Institut National de Recherche en Informatique et en Automatique).