Displacement field estimation using a coupled Gauss-Markov model

James C. Brailean; Aggelos K Katsaggelos

Displacement field estimation using a coupled Gauss-Markov model

James C. Brailean^*, Aggelos K Katsaggelos

^*Corresponding author for this work

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

In this paper, we develop a model-based algorithm for obtaining the maximum a posteriori (MAP) estimate of the displacement vector field (DVF) from two consecutive image frames of an image sequence. To model the DVF, we develop a nonstationary vector field model called the Vector Coupled Gauss-Markov (VCGM) model. The VCGM model is well suited for estimating the DVF, since the resulting estimates preserve the boundaries between the moving and stationary areas in an image sequence. Thus, the model-based MAP estimator that we develop has the ability to adapt to the discontinuities of the DVF. Experiments with a typical video-conferencing sequence demonstrate the superior performance of the proposed algorithm with respect to prediction error and robustness to occlusions.

Original language	English (US)
Title of host publication	Proceedings of SPIE - The International Society for Optical Engineering
Publisher	Publ by Int Soc for Optical Engineering
Pages	170-181
Number of pages	12
Volume	1778
ISBN (Print)	0819409502
State	Published - Dec 1 1992
Event	Imaging Technologies and Applications - Chicago, IL, USA Duration: Mar 19 1992 → Mar 19 1992

Other

Other	Imaging Technologies and Applications
City	Chicago, IL, USA
Period	3/19/92 → 3/19/92

ASJC Scopus subject areas

Electrical and Electronic Engineering
Condensed Matter Physics

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Cite this

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title = "Displacement field estimation using a coupled Gauss-Markov model",

abstract = "In this paper, we develop a model-based algorithm for obtaining the maximum a posteriori (MAP) estimate of the displacement vector field (DVF) from two consecutive image frames of an image sequence. To model the DVF, we develop a nonstationary vector field model called the Vector Coupled Gauss-Markov (VCGM) model. The VCGM model is well suited for estimating the DVF, since the resulting estimates preserve the boundaries between the moving and stationary areas in an image sequence. Thus, the model-based MAP estimator that we develop has the ability to adapt to the discontinuities of the DVF. Experiments with a typical video-conferencing sequence demonstrate the superior performance of the proposed algorithm with respect to prediction error and robustness to occlusions.",

author = "Brailean, {James C.} and Katsaggelos, {Aggelos K}",

year = "1992",

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T1 - Displacement field estimation using a coupled Gauss-Markov model

AU - Brailean, James C.

AU - Katsaggelos, Aggelos K

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N2 - In this paper, we develop a model-based algorithm for obtaining the maximum a posteriori (MAP) estimate of the displacement vector field (DVF) from two consecutive image frames of an image sequence. To model the DVF, we develop a nonstationary vector field model called the Vector Coupled Gauss-Markov (VCGM) model. The VCGM model is well suited for estimating the DVF, since the resulting estimates preserve the boundaries between the moving and stationary areas in an image sequence. Thus, the model-based MAP estimator that we develop has the ability to adapt to the discontinuities of the DVF. Experiments with a typical video-conferencing sequence demonstrate the superior performance of the proposed algorithm with respect to prediction error and robustness to occlusions.

AB - In this paper, we develop a model-based algorithm for obtaining the maximum a posteriori (MAP) estimate of the displacement vector field (DVF) from two consecutive image frames of an image sequence. To model the DVF, we develop a nonstationary vector field model called the Vector Coupled Gauss-Markov (VCGM) model. The VCGM model is well suited for estimating the DVF, since the resulting estimates preserve the boundaries between the moving and stationary areas in an image sequence. Thus, the model-based MAP estimator that we develop has the ability to adapt to the discontinuities of the DVF. Experiments with a typical video-conferencing sequence demonstrate the superior performance of the proposed algorithm with respect to prediction error and robustness to occlusions.

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M3 - Conference contribution

AN - SCOPUS:0027003948

SN - 0819409502

VL - 1778

SP - 170

EP - 181

BT - Proceedings of SPIE - The International Society for Optical Engineering

PB - Publ by Int Soc for Optical Engineering

T2 - Imaging Technologies and Applications

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