This letter incorporates the adaptive kernel Kalman filter (AKKF) into belief propagation (BP) algorithm for multi-target tracking (MTT) in single-sensor systems. The is capable of an unknown and time-varying number targets, presence false alarms, clutter measurement-to-target association uncertainty. Experiment results reveal that proposed method has a favourable performance using generalized ...

We describe a part-based object-recognition framework, specialized to mining complex 3D objects from detailed 3D images. Objects are modeled as a collection of parts together with a pairwise potential function. The algorithm’s key component is an efficient inference algorithm, based on belief propagation, that finds the optimal layout of parts, given some input image. Belief Propagation (BP) – ...

The belief propagation (BP) algorithm has some limitations, including ambiguous edges and textureless regions, and slow convergence speed. To address these problems, we present a novel algorithm that intrinsically improves both the accuracy and the convergence speed of BP. First, traditional BP generally consumes time due to numerous iterations. To reduce the number of iterations, inspired by t...

This paper presents a novel intermediate view synthesis method based on adaptive belief propagation (BP) algorithm and view interpolation. First, we raise the accuracy in disparity estimation by introducing an adaptive BP algorithm. Then, image pairs are divided into three kinds of regions. Finally, intermediate view is obtained by applying a new interpolation method to each region. Experimenta...

The belief propagation (BP) based algorithm is investigated as a potential decoder for both of error correcting codes and lossy compression, which are based on non-monotonic tree-like multilayer perceptron encoders. We discuss that whether the BP can give practical algorithms or not in these schemes. The BP implementations in those kind of fully connected networks unfortunately shows strong lim...

In this paper, we consider the problem of iterative detection and decoding (IDD) for multi-antenna systems using low-density parity-check (LDPC) codes. The proposed IDD system consists of a soft-input soft-output parallel interference (PIC) cancellation scheme with linear minimum mean-square error (MMSE) receive filters and two novel belief propagation (BP) decoding algorithms. The proposed BP ...

It is well known that an arbitrary discrete-variable graphical model of statistical inference defined on a tree, i.e. on a graph without loops, is solved exactly and efficiently by algorithms of the Belief Propagation (BP) type. Extending recent results of Kolmogorov & Wainwright ’05 and Bayati, Shah, & Sharma ’06, we discuss here two cases of the opposite extreme, when BP algorithm finds optim...

The belief propagation (BP) based algorithm is investigated as a potential decoder for both of error correcting codes and lossy compression, which are based on non-monotonic tree-like multilayer perceptron encoders. We discuss that whether the BP can give practical algorithms or not in these schemes. The BP implementations in those kind of fully connected networks unfortunately shows strong lim...

Two decoding procedures combined with a beliefpropagation (BP) decoding algorithm for low-density parity-check codes over the binary erasure channel are presented. These algorithms continue a decoding procedure after the BP decoding algorithm terminates. We derive a condition that our decoding algorithms can correct an erased bit which is uncorrectable by the BP decoding algorithm. We show by s...

We elaborate on a linear-time implementation of Collective-Influence (CI) algorithm introduced by Morone, Makse, Nature 524, 65 (2015) to find the minimal set of influencers in networks via optimal percolation. The computational complexity of CI is O(N log N) when removing nodes one-by-one, made possible through an appropriate data structure to process CI. We introduce two Belief-Propagation (B...