Title :
A recursive nonstationary MAP displacement vector field estimation algorithm
Author :
Brailean, James C. ; Katsaggelos, Aggelos K.
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Northwestern Univ., Evanston, IL, USA
fDate :
4/1/1995 12:00:00 AM
Abstract :
A recursive model-based algorithm for obtaining the maximum a posteriori (MAP) estimate of the displacement vector field (DVF) from successive image frames of an image sequence is presented. To model the DVF, we develop a nonstationary vector field model called the vector coupled Gauss-Markov (VCGM) model. The VCGM model consists of two levels: an upper level, which is made up of several submodels with various characteristics, and a lower level or line process, which governs the transitions between the submodels. A detailed line process is proposed. The VCGM model is well suited for estimating the DVF since the resulting estimates preserve the boundaries between the differently moving areas in an image sequence. A Kalman type estimator results, followed by a decision criterion for choosing the appropriate line process. Several experiments demonstrate the superior performance of the proposed algorithm with respect to prediction error, interpolation error, and robustness to noise
Keywords :
Kalman filters; Markov processes; image sequences; interpolation; maximum likelihood estimation; recursive estimation; Kalman type estimator; VCGM model; decision criterion; displacement vector field estimation algorithm; image frames; image sequence; interpolation error; line process; maximum a posteriori estimate; noise robustness; nonstationary vector field model; performance; prediction error; recursive model-based algorithm; recursive nonstationary MAP; submodels; vector coupled Gauss-Markov model; Gaussian processes; Image sequences; Interpolation; Kalman filters; Layout; Markov random fields; Motion estimation; Noise robustness; Recursive estimation; Stochastic processes;
Journal_Title :
Image Processing, IEEE Transactions on