This text discusses the mathematical foundations of statistical inference for building 3-dimensional models from image and sensor data that contain noise -- a task involving autonomous robots guided by video cameras and sensors. The text employs a theoretical accuracy for the optimization procedure, which maximizes the reliability of estimations based on noise data. 1996 edition.
This text discusses the mathematical foundations of statistical inference for building 3-dimensional models from image and sensor data that contain noise -- a task involving autonomous robots guided by video cameras and sensors. The text employs a theoretical accuracy for the optimization procedure, which maximizes the reliability of estimations based on noise data. 1996 edition.
1. Introduction 2. Fundamentals of Linear Algebra 3. Probabilities and Statistical Estimation 4. Representation of Geometric Objects 5. Geometric Correction 6. 3-D Computation by Stereo Vision 7. Parametric Fitting 8. Optimal Filter 9. Renormalization 10. Applications of Geometric Estimation 11. 3-D Motion Analysis 12. 3-D Interpretation of Optical Flow 13. Information Criterion for Model Selection 14. General Theory of Geometric Estimation References Index
1. Introduction 2. Fundamentals of Linear Algebra 3. Probabilities and Statistical Estimation 4. Representation of Geometric Objects 5. Geometric Correction 6. 3-D Computation by Stereo Vision 7. Parametric Fitting 8. Optimal Filter 9. Renormalization 10. Applications of Geometric Estimation 11. 3-D Motion Analysis 12. 3-D Interpretation of Optical Flow 13. Information Criterion for Model Selection 14. General Theory of Geometric Estimation References Index
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