Background
Zhang, Zhengyou was born on April 1, 1965 in Wenling, China. Arrived in France, 1986. Came to the United States, 1998.
Son of Qinlan and Xiangfeng (Yang) Zhang.
(Appendix 164 3. A 3. A. 1 Approximate Estimation of Funda...)
Appendix 164 3. A 3. A. 1 Approximate Estimation of Fundamental Matrix from General Matrix 164 3. A. 2 Estimation of Affine Transformation 165 4 RECOVERY OF EPIPOLAR GEOMETRY FROM LINE SEGMENTS OR LINES 167 Line Segments or Straight Lines 168 4. 1 4. 2 Solving Motion Using Line Segments Between Two Views 173 4. 2. 1 Overlap of Two Corresponding Line Segments 173 Estimating Motion by Maximizing Overlap 175 4. 2. 2 Implementation Details 4. 2. 3 176 Reconstructing 3D Line Segments 4. 2. 4 179 4. 2. 5 Experimental Results 180 4. 2. 6 Discussions 192 4. 3 Determining Epipolar Geometry of Three Views 194 4. 3. 1 Trifocal Constraints for Point Matches 194 4. 3. 2 Trifocal Constraints for Line Correspondences 199 4. 3. 3 Linear Estimation of K, L, and M Using Points and Lines 200 4. 3. 4 Determining Camera Projection Matrices 201 4. 3. 5 Image Transfer 203 4. 4 Summary 204 5 REDEFINING STEREO, MOTION AND OBJECT RECOGNITION VIA EPIPOLAR GEOMETRY 205 5. 1 Conventional Approaches to Stereo, Motion and Object Recognition 205 5. 1. 1 Stereo 205 5. 1. 2 Motion 206 5. 1. 3 Object Recognition 207 5. 2 Correspondence in Stereo, Motion and Object Recognition as 1D Search 209 5. 2. 1 Stereo Matching 209 xi Contents 5. 2. 2 Motion Correspondence and Segmentation 209 5. 2. 3 3D Object Recognition and Localization 210 Disparity and Spatial Disparity Space 210 5.
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(This is the first book to treat the analysis of 3D dynami...)
This is the first book to treat the analysis of 3D dynamic scenes using a stereovision system. Several approaches are described, for example two different methods for 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 thatmotion estimation can be further improved by the explicit modeling 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.
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(Appendix 164 3. A 3. A. 1 Approximate Estimation of Funda...)
Appendix 164 3. A 3. A. 1 Approximate Estimation of Fundamental Matrix from General Matrix 164 3. A. 2 Estimation of Affine Transformation 165 4 RECOVERY OF EPIPOLAR GEOMETRY FROM LINE SEGMENTS OR LINES 167 Line Segments or Straight Lines 168 4. 1 4. 2 Solving Motion Using Line Segments Between Two Views 173 4. 2. 1 Overlap of Two Corresponding Line Segments 173 Estimating Motion by Maximizing Overlap 175 4. 2. 2 Implementation Details 4. 2. 3 176 Reconstructing 3D Line Segments 4. 2. 4 179 4. 2. 5 Experimental Results 180 4. 2. 6 Discussions 192 4. 3 Determining Epipolar Geometry of Three Views 194 4. 3. 1 Trifocal Constraints for Point Matches 194 4. 3. 2 Trifocal Constraints for Line Correspondences 199 4. 3. 3 Linear Estimation of K, L, and M Using Points and Lines 200 4. 3. 4 Determining Camera Projection Matrices 201 4. 3. 5 Image Transfer 203 4. 4 Summary 204 5 REDEFINING STEREO, MOTION AND OBJECT RECOGNITION VIA EPIPOLAR GEOMETRY 205 5. 1 Conventional Approaches to Stereo, Motion and Object Recognition 205 5. 1. 1 Stereo 205 5. 1. 2 Motion 206 5. 1. 3 Object Recognition 207 5. 2 Correspondence in Stereo, Motion and Object Recognition as 1D Search 209 5. 2. 1 Stereo Matching 209 xi Contents 5. 2. 2 Motion Correspondence and Segmentation 209 5. 2. 3 3D Object Recognition and Localization 210 Disparity and Spatial Disparity Space 210 5.
http://www.amazon.com/gp/product/0792341996/?tag=2022091-20
(Face detection, because of its vast array of applications...)
Face detection, because of its vast array of applications, is one of the most active research areas in computer vision. In this book, we review various approaches to face detection developed in the past decade, with more emphasis on boosting-based learning algorithms. We then present a series of algorithms that are empowered by the statistical view of boosting and the concept of multiple instance learning. We start by describing a boosting learning framework that is capable to handle billions of training examples. It differs from traditional bootstrapping schemes in that no intermediate thresholds need to be set during training, yet the total number of negative examples used for feature selection remains constant and focused (on the poor performing ones). A multiple instance pruning scheme is then adopted to set the intermediate thresholds after boosting learning. This algorithm generates detectors that are both fast and accurate. 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
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(Human faces are familiar to our visual systems. We easily...)
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.
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Zhang, Zhengyou was born on April 1, 1965 in Wenling, China. Arrived in France, 1986. Came to the United States, 1998.
Son of Qinlan and Xiangfeng (Yang) Zhang.
Bachelor of Science, University Zhejiang, 1985. Diploma of Advanced Studies, University Nancy, 1987. Doctor of Philosophy, University Paris XI, 1990.
Habilitation, University Paris XI, 1994.
Assistant researcher Institut National de Recherche en Informatique et Automatique, Rocquencourt, France, 1987—1990, researcher Sophia-Antipolis, France, 1990—1991, senior researcher France, 1991—1998. Affiliate professor University Washington, since 1996. Principal researcher Microsoft Research, Redmond, Washington, 1998—1999, principal, since 1999.
Doctor of Philosophy supervisor University Paris XI, Orsay, 1994-1998. Program committee member International Symposium Young Investigators, Beijing, 1994, Institute of Electrical and Electronics Engineers Conference Computer Vision and Pattern Recognition, California, 1996, Colorado, 1999, 2000, 2001, 2003, 2005, 2006, Institute of Electrical and Electronics Engineers Conference Automatic Face and Gesture Recognition, Japan, 1998, Institute of Electrical and Electronics Engineers Workshop Applications Computer Vision, New Jersey, 1998, International Conference Computer Vision, 2001, 2005, European Conference Computer Vision, 2006. Area chair and demo chair, International Conference Computer Vision, Nice, France, 2003.
Program chair Asian Conference Computer Vision, Jeju, Korea, 2004, Institute of Electrical and Electronics Engineers Workshop Multimedia Signal Processing, Victoria, Canada, 2006, Institute of Electrical and Electronics Engineers Workshop Motion, Video Computing, Austin, Texas, 2007. Program co-chair 8th International Conference Development and Learning, Shanghai, 2009, International Conference Multimedia and Expo, 2010, Association for Computing Machinery (ACM) International Conference Multimedia, 2010, Association for Computing Machinery (ACM) International Conference Multimodal Interfaces, 2010. General co-chair International Workshop Multimedia Signal Processing, Hangzhou, China, since 2010.
Invited researcher ATR, Japan, 1996-1997. Guest research professor Chinese Academy of Sciences. Part-time professor Northern Jiaotong University, Beijing.
Guest professor, Zhejiang University. Adjunct associate professor University Southern California.
(he problem of analyzing sequences of images to extract th...)
(Face detection, because of its vast array of applications...)
(This is the first book to treat the analysis of 3D dynami...)
(Human faces are familiar to our visual systems. We easily...)
(Appendix 164 3. A 3. A. 1 Approximate Estimation of Funda...)
(Appendix 164 3. A 3. A. 1 Approximate Estimation of Funda...)
Member Institute of Electrical and Electronics Engineers (senior, conference on atomic face and gesture recognition 1998, conference on computer vision and pattern recognition 1999), Chinese Artificial Vision Association (founder, chairman 1993-1995), Association Computer Machinery.
Married Ming-Yue Xie, April 22, 1988. Children: Shuting Rosaline, Laetitia Xiaoling, Stephanie Xiaoying.