Linear response formulas for the generalized belief propagation in approximate inference are derived by using generalized belief propagation. The linear response formulas can give...
Abstract. 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 m...
— Smoothing and optimization approaches are an effective means for solving the simultaneous localization and mapping (SLAM) problem. Most of the existing techniques focus mainly ...
Gian Diego Tipaldi, Giorgio Grisetti, Wolfram Burg...
— This paper proposes a new approach for decoding LDPC codes over MISO channels. Since in an nT × 1 MISO system with a modulation of alphabet size 2M, nT transmitted symbols are...
Amir H. Djahanshahi, Paul H. Siegel, Laurence B. M...
Abstract— In this paper we present an efficient system-onchip implementation of a 1-Gbps LDPC decoder for 4G (or beyond 3G) wireless standards. The decoder has a scalable datapa...
Belief propagation has become a popular technique for solving computer vision problems, such as stereo estimation and image denoising. However, it requires large memory and bandwi...
We introduce a novel data-driven mean-shift belief propagation
(DDMSBP) method for non-Gaussian MRFs, which
often arise in computer vision applications. With the aid
of scale sp...
The Shape-from-Shading [SfS] problem infers shape from reflected light, collected using a camera at a single point in space only. Reflected light alone does not provide sufficient ...
Belief Propagation (BP) can be very useful and efficient for performing approximate inference on graphs. But when the graph is very highly connected with strong conflicting intera...
Belief propagation over pairwise connected Markov Random Fields has become a widely used approach, and has been successfully applied to several important computer vision problems....