Maximum a posteriori displacement field estimation in quantum-limited image sequences

1993 ◽  
Author(s):  
Cheuk L. Chan ◽  
James C. Brailean ◽  
Aggelos K. Katsaggelos ◽  
Alan V. Sahakian
Keyword(s):  
Displacement Field ◽  
Image Sequences ◽  
Maximum A Posteriori ◽  
A Posteriori ◽  
Field Estimation

Displacement Field Estimation in Noisy Image Sequences

1992 ◽  
pp. 1319-1322 ◽  
Author(s):  
James C. Brailean ◽  
Aggelos K. Katsaggelos
Keyword(s):  
Displacement Field ◽  
Image Sequences ◽  
Noisy Image ◽  
Field Estimation

scholarly journals Level Set Segmentation of Medical Images Based on Local Region Statistics and Maximum a Posteriori Probability

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Wenchao Cui ◽  
Yi Wang ◽  
Tao Lei ◽  
Yangyu Fan ◽  
Yan Feng
Keyword(s):  
Objective Function ◽  
Level Set ◽  
Bias Field ◽  
Posteriori Probability ◽  
Maximum A Posteriori ◽  
A Posteriori ◽  
Image Domain ◽  
A Posteriori Probability ◽  
Global Criterion ◽  
Field Estimation

This paper presents a variational level set method for simultaneous segmentation and bias field estimation of medical images with intensity inhomogeneity. In our model, the statistics of image intensities belonging to each different tissue in local regions are characterized by Gaussian distributions with different means and variances. According to maximum a posteriori probability (MAP) and Bayes’ rule, we first derive a local objective function for image intensities in a neighborhood around each pixel. Then this local objective function is integrated with respect to the neighborhood center over the entire image domain to give a global criterion. In level set framework, this global criterion defines an energy in terms of the level set functions that represent a partition of the image domain and a bias field that accounts for the intensity inhomogeneity of the image. Therefore, image segmentation and bias field estimation are simultaneously achieved via a level set evolution process. Experimental results for synthetic and real images show desirable performances of our method.

scholarly journals Threshold Computation for Spatially Coupled Turbo-Like Codes on the AWGN Channel

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 240
Author(s):  
Muhammad Umar Farooq ◽  
Alexandre Graell i Amat ◽  
Michael Lentmaier
Keyword(s):  
Gaussian Noise ◽  
Belief Propagation ◽  
Additive White Gaussian Noise ◽  
Maximum A Posteriori ◽  
Prediction Methods ◽  
Threshold Analysis ◽  
A Posteriori ◽  
Awgn Channel ◽  
Efficient Prediction ◽  
Binary Erasure Channel

In this paper, we perform a belief propagation (BP) decoding threshold analysis of spatially coupled (SC) turbo-like codes (TCs) (SC-TCs) on the additive white Gaussian noise (AWGN) channel. We review Monte-Carlo density evolution (MC-DE) and efficient prediction methods, which determine the BP thresholds of SC-TCs over the AWGN channel. We demonstrate that instead of performing time-consuming MC-DE computations, the BP threshold of SC-TCs over the AWGN channel can be predicted very efficiently from their binary erasure channel (BEC) thresholds. From threshold results, we conjecture that the similarity of MC-DE and predicted thresholds is related to the threshold saturation capability as well as capacity-approaching maximum a posteriori (MAP) performance of an SC-TC ensemble.

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