Paper
22 October 1993 Maximum a posteriori displacement field estimation in quantum-limited image sequences
Cheuk L. Chan, James C. Brailean, Aggelos K. Katsaggelos, Alan V. Sahakian
Author Affiliations +
Proceedings Volume 2094, Visual Communications and Image Processing '93; (1993) https://doi.org/10.1117/12.157958
Event: Visual Communications and Image Processing '93, 1993, Cambridge, MA, United States
Abstract
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 of the field are taken into account through the introduction of a line 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.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Cheuk L. Chan, James C. Brailean, Aggelos K. Katsaggelos, and Alan V. Sahakian "Maximum a posteriori displacement field estimation in quantum-limited image sequences", Proc. SPIE 2094, Visual Communications and Image Processing '93, (22 October 1993); https://doi.org/10.1117/12.157958
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Cited by 2 scholarly publications.
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KEYWORDS
Image processing

Algorithm development

Stochastic processes

Cadmium

Image analysis

Medical imaging

X-ray imaging

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