TY - GEN
T1 - Occlusion and nonstationary displacement field estimation in quantum-limited image sequences
AU - Chan, Cheuk L.
AU - Brailean, James C.
AU - Katsaggelos, Aggelos K.
PY - 1995
Y1 - 1995
N2 - In this paper, we develop an algorithm for obtaining the maximum a posteriori (MAP) estimate of the displacement vector field (DVF) from two consecutive image frames of an image sequence acquired under quantum-limited conditions. The estimation of the DVF has applications in temporal filtering, object tracking and frame registration in low- light level image sequences as well as low-dose clinical x-ray image sequences. The quantum-limited effect is modeled as an undesirable, Poisson-distributed, signal-dependent noise artifact. The specification of priors for the DVF allows a smoothness constraint for the vector field. In addition, discontinuities and areas corresponding to occlusions which are present in the field are taken into account through the introduction of both a line process and an occlusion process for neighboring vectors. A Bayesian formulation is used in this paper to estimate the DVF and a block component algorithm is employed in obtaining a solution. Several experiments involving a phantom sequence show the effectiveness of this estimator in obtaining the DVF under severe quantum noise conditions.
AB - In this paper, we develop an algorithm for obtaining the maximum a posteriori (MAP) estimate of the displacement vector field (DVF) from two consecutive image frames of an image sequence acquired under quantum-limited conditions. The estimation of the DVF has applications in temporal filtering, object tracking and frame registration in low- light level image sequences as well as low-dose clinical x-ray image sequences. The quantum-limited effect is modeled as an undesirable, Poisson-distributed, signal-dependent noise artifact. The specification of priors for the DVF allows a smoothness constraint for the vector field. In addition, discontinuities and areas corresponding to occlusions which are present in the field are taken into account through the introduction of both a line process and an occlusion process for neighboring vectors. A Bayesian formulation is used in this paper to estimate the DVF and a block component algorithm is employed in obtaining a solution. Several experiments involving a phantom sequence show the effectiveness of this estimator in obtaining the DVF under severe quantum noise conditions.
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M3 - Conference contribution
AN - SCOPUS:0029232295
SN - 0819418587
T3 - Proceedings of SPIE - The International Society for Optical Engineering
SP - 962
EP - 973
BT - Proceedings of SPIE - The International Society for Optical Engineering
PB - Society of Photo-Optical Instrumentation Engineers
T2 - Visual Communications and Image Processing '95
Y2 - 24 May 1995 through 26 May 1995
ER -