New algorithms for variable time delay and nonuniform image motion estimation
著者
書誌事項
New algorithms for variable time delay and nonuniform image motion estimation
（Computer engineering and computer science）
Ablex Pub. Corp., c1994
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注記
Includes bibliographical references (p. 157-159) and indexes
内容説明・目次
内容説明
This text introduces and investigates estimation algorithms for signal registration. Signal registration can be defined as the problem of estimating a varying displacement between two random processes. This problem has great importance in many applications, perhaps the most familiar of which lies in the field of passive sonar. The bearing of the signal source is related to the time delay (displacement) between two random waveforms. In the general signal registration problem, the time delay is itself a random function of time. Part one of this book is devoted to the problem of variable time delay estimation. Another application of the signal registration lies in image processing where registration is called "motion estimation". By determining the relative displacement (motion) between the intensities of consecutive image frames, one can encode an image more efficiently. In this application, the displacement is a position-dependent vector process. Part two discusses the problem of nonuniform image motion estimation.
目次
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
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