动态视觉

书籍目录

PrefacePART  I  BACKGROUND　1  About Face　　1.1  The Visual Face　　1.2  The Changing Face　　1.3  Computing Faces　　1.4  Biological Perspectives .　　1.5  The Approach　2  Perception and Representation　　2.1  A Distal Object　　2.2  Representation by 3D Reconstruction　　2.3  Two-dimensional View-based Representation　　2.4  Image Template-based Representation　　2.5  The Correspondence Problem and Alignment　　2.6  Biological Perspectives　　2.7  Discussion　3  Learning under Uncertainty　　3.1  Statistical Learning　　3.2  Learning as Function Approximation　　3.3  Bayesian Inference and MAP Classification　　3.4  Learning as Density Estimation      3.4.1  Parametric Models      3.4.2  Non-parametric Models      3.4.3  Semi-parametric Models　　3.5  Unsupervised Learning without Density Estimation      3.5.1  Dimensionality Reduction      3.5.2  Clustering　　3.6  Linear Classification and Regression       3.6.1  Least-squares      3.6.2  Linear Support Vector Machines　3.7  Non-linear Classification and Regression      3.7.1  Multi-layer Networks      3.7.2  Support Vector Machines　3.8  Adaptation　3.9  Biological Perspectives　3.10 DiscussionPART  II  FROM  SENSORY  TO  MEANINGFUL  PERCEPTION　4  Selective Attention: Where to Look　　4.1  Pre-attentive Visual Cues from Motion .      4.1.1  Measuring Temporal Change      4.1.2  Motion Estimation　　4.2  Learning Object-based Colour Cues      4.2.1  Colour Spaces      4.2.2  Colour Density Models　　4.3  Perceptual Grouping for Selective Attention　　4.4  Data Fusion for Perceptual Grouping　　4.5  Temporal Matching and Tracking　　4.6  Biological Perspectives　　4.7  Discussion　5  A Face Model: What to Look For　　5.1  Person-independent Face Models for Detection      5.1.1  Feature-based Models      5.1.2  Holistic Models        5.1.3  The Face Class 　　5.2  Modelling the Face Class      5.2.1  Principal Components Analysis for a Face Model      5.2.2  Density Estimation in Local PCA Spaces　　5.3  Modelling a Near-face Class 　　5.4  Learning a Decision Boundary  　……  6  Undersanding Pose  7  Prediction and  AdaptationPART III  MODELS OF IKDENTITY  8  Single-View Identification  9  Multi-View Identification  10  Identifying Moving FacesPART IV PERCEPTION IN CONTEXT  11  Perceptual Integration  12  Beyond FacesPART V  APPENDICES  A Databases  B Commercial Systems  C Mathematical DetailsBibliographyIndex

作者简介

A description of models and algorithms that are capable of performing face recognition in a dynamic setting. The key question is how to design computer-vision and machine-learning algorithms that can operate robustly and quickly under poorly-controlled and changing conditions. Consideration of face recognition as a problem in dynamic vision is perhaps both novel and important. The algorithms described have numerous potential applications in areas such as visual surveillance, verification, access control, video-conferencing, multimedia and visually-mediated interaction.

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