Statistical Optimization For Geometric Computation: Theory And Practice (machine Intelligence And Pattern Recognition)

This book discusses mathematical foundations of statistical inference for building a 3-D model of the environment from image and sensor data that contain noise - a central task for autonomous robots guided by video cameras and sensors. A theoretical accuracy bound is derived for the optimization procedure for maximizing the reliability of the estimation based on noisy data, and practical computational schemes that attain that bound are derived. Many synthetic and real data examples are given to demonstrate that conventional methods are not optimal and how accuracy improves if truly optimal methods are employed. Institutions to benefit from this book include, University departments related to computer science, information processing, image processing, robotics and mechatronics, governmental research organizations for computer-related advanced technology and corporate laboratories of computer and electronic industries Content: Preface Pages v-vi Kenichi Kanatani Chapter 1 Introduction Original Research Article Pages 1-26 Chapter 2 Fundamentals of linear algebra Original Research Article Pages 27-60 Chapter 3 Probabilities and statistical estimation Original Research Article Pages 61-93 Chapter 4 Representation of geometric objects Original Research Article Pages 95-130 Chapter 5 Geometric correction Original Research Article Pages 131-170 Chapter 6 3-D computation by stereo vision Original Research Article Pages 171-207 Chapter 7 Parametric fitting Original Research Article Pages 209-246 Chapter 8 Optimal filter Original Research Article Pages 247-265 Chapter 9 Renormalization Original Research Article Pages 267-294 Chapter 10 Applications of geometric estimation Original Research Article Pages 295-323 Chapter 11 3-D motion analysis Original Research Article Pages 325-368 Chapter 12 3-D interpretation of optical flow Original Research Article Pages 369-414 Chapter 13 Information criterion for model selection Original Research Article Pages 415-450 Chapter 14 General theory of geometric estimation Original Research Article Pages 451-499 Index Pages 501-509