New Algorithms for Variable Time Delay and Nonuniform Image Motion Estimation

Acknowledgements          ix

1  Introduction to Signal Registration          1

PART I     VARIABLE TIME DELAY ESTIMATION          3

2  Introduction to Time Delay Estimation          5

3  A New Recursive Estimator          9 

4  Delay Estimation based on the MAP Criterion          13

    1. Implementation using the MAP Estimation          13

    2. Estimation Analysis          16

    3. Simulation Experiments          19

5. Delay Estimation based on the ML Criterion          46

    1. The Generalized Maximum Likelihood Algorithm (GML)          46

    2. Implementation using the GML Estimator          48

    3. Simulation Experiments          50

6  Delay Estimation Based on the MMSE Criterion          60

    1. The Minimum Mean Squared Error (MMSE1) Algorithm          60 

    2. Implementation using the MMSE1 Algorithm          62

    3. Simulation Experiments using the MMSE1 Algorithm          62

    4. The Minimum Mean Squared Error (MMSE2) Algorithm          65

    5. Simulation Experiments using the MMSE2 Algorithm          71

7  Concluding Remarks on Part 1          84

PART II     NONUNIFORM IMAGE MOTION ESTIMATION          87

8  Introduction to Image Motion Estimation          89

9  Motion Estimation based on the ML Criterion          92

    1. The Generalized Maximum Likelihood Algorithm in Two-Dimensional Space          92

    2. Implementation Using the GML Algorithm          95

    3. Convergence of the GML Algorithm          98

    4. Simulation Experiments          103

10  Motion Estimation Based on the MAP Criterion          112

      1. The Maximum a Posteriori Algorithm          112

      2. Implementation Using the MAP Algorithm          115

      3. Determination of [A greek symbol]1 and [B greek symbol]1 in the Motion Estimator (10.20)          117

      4. Simulation Experiments          120

11  Motion Estimation in Transformed Domains          125

      1. The Transformed Domain Maximum Likelihood (TDML) Algorithm          125

      2. Implementation using the TDML Algorithm          126

      3. Simulation Experiments          128

12  Estimation of the Motion Coefficients Using Kalman Filtering          132 

      1. Motion Estimation with Kalman Filtering          132

      2. Identification of the Delectable Moving Pixels from Noisy Data          133

      3. Representation of the Reduced-Dimension Image Motion Coefficients in State Space          137 

      4. Generation of the Innovations Process          139

      5. Implementation of the Kalman Filter          141 

      6. Results          143

13 Concluding Remarks on Part II          149

Appendix A.     An Adaptive Realization of the Generalized Maximum Likelihood Algorithm          151

References          157

Author Index          161

Subject Index          163