基于多模方法的位移矢量场自适应估计

陆明俊; 王润生

基于多模方法的位移矢量场自适应估计

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出版历程
- 收稿日期: 1999-02-26
- 修回日期: 1999-08-19
- 刊出日期: 2000-11-19

ADAPTIVE DISPLACEMENT VECTOR FIELD ESTIMATION BASED ON MULTIPLE MODEL APPROACH

摘要

摘要: 半平面CGM(halfplanecompoundGauss-Markov)模型是以线过程(lineprocess)标识不同的子模型，适合于描述平稳和非平稳的位移矢量场DVF(displacementvectorfield)；半平面MRF(Markovrandomfield)模型描述线过程的分布，以确定各个子模型的先验概率。由此，本文提出一种基于多模方法的递归自适应DVF估计算法。
- 位移矢量场;MRF模型;多模方法
Abstract: Each submodel of a half plane compound Gauss-Markov model, which is identified by a line process, is suitable for describing stationary or nonstationary DVF(displacement vector field); In order to determine the a priori distribution of these submodels, a half plane Markov random field model is utilized to describe the distribution of the line process. Thus, a recursive and adaptive DVF estimation algorithm based on multiple model approach is developed in this paper.

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参考文献(1)

Brailean J C, Katsaggelos A K. A recursive nonstationary MAP displacement vector field estimation algorithm. IEEE Trans. on Image Processing, 1995, IP-4(4): 416-429.[2]Efstratiadis S N, Katsaggelos A K. Nonstationary AR modeling and constrained recursive estimation of the displacement field. IEEE Trans. on Circuits and Systems for Video Technology,1992, CASVT-2(4): 334-346.[3]Stuller J, Krishnamurthy G. Kalman filter formulation of low-level television image motion estimation[J].Comp. Vision. Graph. Image Proc.1983, 21(3):169-204[4]Zhang J, Hanauer G G. The application of mean field theory to image motion estimation. IEEE Trans. on Image Processing, 1995, IP-4(1): 19-33.[5]Konrad J, Dubois E. Bayesian estimation of motion vector fields. IEEE Trans. on Pattern Anal. Mach. Intell., 1992, PAMI-14(9): 910-927.[6]Lainiotis D G. Partitioning: A unifying framework for adaptive systems I: Estimation[J].Proc.IEEE.1976, 64(8):1126-1143[7]Zhang J. Parameter reduction for the compound Gauss-Markov model. IEEE Trans. on Image Processing, 1995, IP-4(3): 382-386.[8]Jeng F C, Woods J W. Simulated annealing in compound Gaussian random fields. IEEE Trans.on Inform. Theory,1990, IT-36(1): 94-107.[9]Jeng F C, Woods J W. Compound Gauss-Markov random fields for image estimation. IEEE Trans. on Signal Processing, 1991, SP-39(3): 683-697.[10]Lainiotis D G. Optimal adaptive estimation: Structure and parameter adaptation. IEEE Trans.on Automat. Contr., 1971, AC-16(2): 160-170.[11]Sims F L, Lainiotis D G, Magill D T. Recursive algorithm for the calculation of the adaptive Kalman filter weighting coefficients. IEEE Trans. on Automat. Contr., 1969, AC-14(2): 215-218.[12]Magill D T. Optimal adaptive estimation of sampled stochastic processes. IEEE Trans. on Automat. Contr., 1965, AC-10(4): 434-439.[13]Tugnait J K. Detection and estimation for abruptly changing systems[J].Automatica.1982, 18(5):607-615

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