An efficient articulated body tracking algorithm is proposed in this paper. Due to the high dimensionality of human-body motion, current articulated tracking algorithms based on sampling [1], belief propagation (BP) [2], or non-parametric belief propagation (NBP) [3], are very slow. To accelerate the articulated tracking algorithm, we adapted belief propagation according to the dynamics of arti...

Belief propagation (BP) is one of the bestknown graphical model for inference in statistical physics, artificial intelligence, computer vision, etc. Furthermore, a recent research in distributed sensor network localization showed us that BP is an efficient way to obtain sensor location as well as appropriate uncertainty. However, BP convergence is not guaranteed in a network with loops. In this...

How can we tell when accounts are fake or real in a social network? And how can we tell which accounts belong to liberal, conservative or centrist users? Often, we can answer such questions and label nodes in a network based on the labels of their neighbors and appropriate assumptions of homophily (”birds of a feather flock together”) or heterophily (”opposites attract”). One of the most widely...

New decoding algorithms for binary linear codes based on the concave-convex procedure are presented. Numerical experiments show that the proposed decoding algorithms surpass Belief Propagation (BP) decoding in error performance. Average computational complexity of one of the proposed decoding algorithms is only a few times greater than that of the BP decoding. key words: concave-convex procedur...

Belief propagation (BP) is the calculation method which enables us to obtain the marginal probabilities with a tractable computational cost. BP is known to provide true marginal probabilities when the graph describing the target distribution has a tree structure, while do approximate marginal probabilities when the graph has loops. The accuracy of loopy belief propagation (LBP) has been studied...

In this lecture, we study the Belief propagation algorithm(BP) and the Max Product algorithm(MP). Last lecture reminds us of that in MRF, computing the marginal probabilities of random variables and Maximum A Posteriori(MAP) assignment is important. The Belief Propagation algorithm is a popular algorithm that is used to compute marginal probability of random variables. Max Product algorithm is ...

In this paper Hard Decision and Soft Decision decoding techniques for Quasi-Cyclic-Low Density Parity Check (QC-LDPC) code and Low Density Parity Check (LDPC) code is introduced. QC-LDPC code is proposed to reduce the complexity of the Low Density Parity Check code while obtaining the similar performance. The decoding processes of these codes are easy to simplify and implement. The algorithms u...

The paper investigates parameterized approximate message-passing schemes that are based on bounded inference and are inspired by Pearl's belief propagation algorithm (BP). We start with the bounded inference mini-clustering algorithm and then move to the iterative scheme called Iterative Join-Graph Propagation (IJGP), that combines both iteration and bounded inference. Algorithm IJGP belongs to...

Kudekar et al. proved an interesting result in low-density parity-check (LDPC) convolutional codes: The belief-propagation (BP) threshold is boosted to the maximum-a-posteriori (MAP) threshold by spatial coupling. Furthermore, the authors showed that the BP threshold for code-division multiple-access (CDMA) systems is improved up to the optimal one via spatial coupling. In this letter, a phenom...

“Inference” problem arise in computer vision, AI, statistical physics and coding theory. The rationale behind the belief propagation is an efficient way to solve inference problems by propagating local messages around neighborhoods [5]. Although researchers proved that the belief propagation (BP) converges to a unique fixed point (fixed probabilistic belief) on singly connected graphs [1], they...