scholarly journals Slice Sampling Particle Belief Propagation

2013 ◽  
Author(s):  
Oliver Muller ◽  
Michael Ying Yang ◽  
Bodo Rosenhahn
Keyword(s):  
Belief Propagation ◽  
Slice Sampling

Graphical Model MAP Inference with Continuous Label Space in Computer Vision

2018 ◽  
Author(s):  
Oliver Müller
Keyword(s):  
Graphical Model ◽  
Belief Propagation ◽  
Fundamental Problem ◽  
Parameter Tuning ◽  
3D Models ◽  
Target Object ◽  
State Variables ◽  
Slice Sampling ◽  
Global Consistency ◽  
Continuous State

This thesis deals with monocular object tracking from video sequences. The goal is to improve tracking of previously unseen non-rigid objects under severe articulations without relying on prior information such as detailed 3D models and without expensive offline training with manual annotations. The proposed framework tracks highly articulated objects by decomposing the target object into small parts and apply online tracking. Drift, which is a fundamental problem of online trackers, is reduced by incorporating image segmentation cues and by using a novel global consistency prior. Joint tracking and segmentation is formulated as a high-order probabilistic graphical model over continuous state variables. A novel inference method is proposed, called S-PBP, combining slice sampling and particle belief propagation. It is shown that slice sampling leads to fast convergence and does not rely on hyper-parameter tuning as opposed to competing approaches based on Metropolis-Hastings or heuristi...

An Accelerated Cue Combination Principle Accounts for Multi-cue Depth Perception

2020 ◽  
Vol 3 (1) ◽  
pp. 10501-1-10501-9
Author(s):  
Christopher W. Tyler
Keyword(s):  
Belief Propagation ◽  
Probability Distributions ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Fusion Model ◽  
Cue Combination ◽  
Depth Estimate ◽  
Appropriate Behavior ◽  
Depth Cues ◽  
Bayesian Averaging

Abstract For the visual world in which we operate, the core issue is to conceptualize how its three-dimensional structure is encoded through the neural computation of multiple depth cues and their integration to a unitary depth structure. One approach to this issue is the full Bayesian model of scene understanding, but this is shown to require selection from the implausibly large number of possible scenes. An alternative approach is to propagate the implied depth structure solution for the scene through the “belief propagation” algorithm on general probability distributions. However, a more efficient model of local slant propagation is developed as an alternative.The overall depth percept must be derived from the combination of all available depth cues, but a simple linear summation rule across, say, a dozen different depth cues, would massively overestimate the perceived depth in the scene in cases where each cue alone provides a close-to-veridical depth estimate. On the other hand, a Bayesian averaging or “modified weak fusion” model for depth cue combination does not provide for the observed enhancement of perceived depth from weak depth cues. Thus, the current models do not account for the empirical properties of perceived depth from multiple depth cues.The present analysis shows that these problems can be addressed by an asymptotic, or hyperbolic Minkowski, approach to cue combination. With appropriate parameters, this first-order rule gives strong summation for a few depth cues, but the effect of an increasing number of cues beyond that remains too weak to account for the available degree of perceived depth magnitude. Finally, an accelerated asymptotic rule is proposed to match the empirical strength of perceived depth as measured, with appropriate behavior for any number of depth cues.

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.

scholarly journals Low-complexity Large MIMO Detection via Layered Belief Propagation in Beam Domain

2021 ◽  
pp. 1-1
Author(s):  
Takumi Takahashi ◽  
Antti Tolli ◽  
Shinsuke Ibi ◽  
Seiichi Sampei
Keyword(s):  
Belief Propagation ◽  
Low Complexity ◽  
Mimo Detection

scholarly journals Perturbed Adaptive Belief Propagation Decoding for High-Density Parity-Check Codes

2020 ◽  
pp. 1-1
Author(s):  
Li Deng ◽  
Zilong Liu ◽  
Yong Liang Guan ◽  
Xiaobei Liu ◽  
Chaudhry Adnan Aslam ◽  
...  
Keyword(s):  
Belief Propagation ◽  
High Density ◽  
Parity Check ◽  
Parity Check Codes

scholarly journals FAULT-TOLERANT MULTI-AGENT EXACT BELIEF PROPAGATION

2009 ◽  
Vol 25 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Xiangdong An ◽  
Nick Cercone
Keyword(s):  
Belief Propagation ◽  
Fault Tolerant ◽  
Multi Agent

Bounds on the Belief Propagation Threshold of Non-Binary LDPC Codes

2016 ◽  
Vol 62 (5) ◽  
pp. 2639-2657 ◽  
Author(s):  
Leonid Geller ◽  
David Burshtein
Keyword(s):  
Belief Propagation ◽  
Ldpc Codes
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