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This classroom-tested and easy-to-understand textbook/reference describes the state of the art in 3D reconstruction from multiple images, taking into consideration all aspects of programming and implementation. Unlike other computer vision textbooks, this guide takes a unique approach in which the initial focus is on practical application and the procedures necessary to actually build a computer vision system. The theoretical background is then briefly explained afterwards, highlighting how one can quickly and simply obtain the desired result without knowing the derivation of the mathematical detail. Features: reviews the fundamental algorithms underlying computer vision; describes the latest techniques for 3D reconstruction from multiple images; summarizes the mathematical theory behind statistical error analysis for general geometric estimation problems; presents derivations at the end of each chapter, with solutions supplied at the end of the book; provides additional material at anassociated website.
List of contents
Introduction.- Part I: Fundamental Algorithms for Computer Vision.- Ellipse Fitting.- Fundamental Matrix Computation.- Triangulation.- 3D Reconstruction from Two Views.- Homography Computation.- Planar Triangulation.- 3D Reconstruction of a Plane.- Ellipse Analysis and 3D Computation of Circles.- Part II: Multiview 3D Reconstruction.- Multiview Triangulation.- Bundle Adjustment.- Self-calibration of Affine Cameras.- Self-calibration of Perspective Cameras.- Part III: Mathematical Foundation of Geometric Estimation.- Accuracy of Geometric Estimation.- Maximum Likelihood and Geometric Estimation.- Theoretical Accuracy Limit.- Solutions.
About the author
Dr. Kenichi Kanatani is a Professor Emeritus at Okayama University, Japan. Drs. Yasuyuki Sugaya and Yasushi Kanazawa are Associate Professors in the Department of Computer Science and Engineering at Toyohashi University of Technology, Japan.
Summary
This classroom-tested and easy-to-understand textbook/reference describes the state of the art in 3D reconstruction from multiple images, taking into consideration all aspects of programming and implementation. Unlike other computer vision textbooks, this guide takes a unique approach in which the initial focus is on practical application and the procedures necessary to actually build a computer vision system. The theoretical background is then briefly explained afterwards, highlighting how one can quickly and simply obtain the desired result without knowing the derivation of the mathematical detail. Features: reviews the fundamental algorithms underlying computer vision; describes the latest techniques for 3D reconstruction from multiple images; summarizes the mathematical theory behind statistical error analysis for general geometric estimation problems; presents derivations at the end of each chapter, with solutions supplied at the end of the book; provides additional material at anassociated website.
