&lt;title&gt;Maximum a posteriori displacement field estimation in quantum-limited image sequences&lt;/title&gt;

Chan, Cheuk L.; Brailean, J.C.; Katsaggelos, Aggelos K.; Sahakian, Alan V.

doi:10.1117/12.157958

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The occlusion (Le., temporally unpredictable) areas, which are newly exposed or covered by the moving object, can also be detected together with motion estimation [22,23,21]. The recent results show that even if the image is corrupted with signal dependent [24] or Gaussian noise with some blur [25], Markov modeled motion estimation methods achieve good performances.…”

Section: D Motion Estimation and Compensationmentioning

confidence: 99%

Gibbs Model Based 3D Motion and Structure Estimation for Object-Based Video Coding Applications

Alatan

Onural

1997

Video Data Compression for Multimedia Computing

View full text Add to dashboard Cite

Motion analysis is essential for any video coding scheme. A moving object in a 3D environment can be analyzed better by a 3D motion model instead of 2D models, and better modeling might lead to improved coding efficiency. Gibbs formulated joint segmentation and estimation of 2D motion not only improves the performance of each stage, but also generates robust point correspondences which are necessary for rigid 3D motion estimation algorithms. Estimated rigid 3D motion parameters of a segmented object are used to find the 3D structure of those objects by minimizing another Gibbs energy. Such an approach achieves error immunity compared to linear algorithms. A more general (non-rigid) motion model can also be proposed using Gibbs formulation which permits local elastic interactions in contrast to ultimately tight rigidity between object points. Experimental results are promising for both rigid and non-rigid 3D motion models and put these models forward as strong candidates to be used in object-based coding algorithms.

show abstract

Section: D Motion Estimation and Compensationmentioning

confidence: 99%

Gibbs Model Based 3D Motion and Structure Estimation for Object-Based Video Coding Applications

Alatan

Onural

1997

Video Data Compression for Multimedia Computing

View full text Add to dashboard Cite

show abstract

“…In our previous works, we modeled the DVF as both an unknown deterministic parameter [14] and a nonstationary Markov random field [15], while the intensity field was modeled as a doubly stochastic Poisson point process characteristic of a quantum-limited event. The advantage of the Markov random field model over the deterministic model is its ability to characterize the discontinuities which are present iii the DVF.…”

Section: Introductionmentioning

confidence: 99%

“…Poisson noise simulator, after[15] Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/15/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx…”

mentioning

confidence: 99%

<title>Occlusion and nonstationary displacement field estimation in quantum-limited image sequences</title>

Chan¹,

Brailean

Katsaggelos³

1995

SPIE Proceedings

Self Cite

View full text Add to dashboard Cite

In this paper, we develop an algorithm for obtaining the maximum a posierzorz (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.

show abstract

Iterative maximum likelihood displacement field estimation in quantum-limited image sequences

Chan¹,

Katsaggelos

1995

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

We develop an algorithm for obtaining the maximum likelihood (ML) estimate of the displacement vector field (DVP) 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, stereo matching, and frame registration in low-light level image sequences as well as low-dose clinical X-ray image sequences. In the latter case, a controlled X-ray dosage reduction may be utilized to lower the radiation exposure to the patient and the medical staff. The quantum-limited effect is modeled as an undesirable, Poisson-distributed, signal-dependent noise artifact. A Fisher-Bayesian formulation is used to estimate the DVF and a block component search algorithm is employed in obtaining the solution. Several experiments involving a phantom sequence and a teleconferencing image sequence with realistic motion demonstrate the effectiveness of this estimator in obtaining the DVF under severe quantum noise conditions (20-25 events/pixel).

show abstract

<title>Maximum a posteriori displacement field estimation in quantum-limited image sequences</title>

Cited by 3 publications

References 0 publications

Gibbs Model Based 3D Motion and Structure Estimation for Object-Based Video Coding Applications

Gibbs Model Based 3D Motion and Structure Estimation for Object-Based Video Coding Applications

<title>Occlusion and nonstationary displacement field estimation in quantum-limited image sequences</title>

Iterative maximum likelihood displacement field estimation in quantum-limited image sequences

Contact Info

Product

Resources

About