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We propose a novel energy function and use dual-layer loopy belief propagation to minimize it where the correspondence, the feature scale and rotation parameters are solved simultaneously. Our ...

This propagation model is unrolled for a certain num- ber of time steps and the ﬁnal per-node representation is used for predicting the semantic class of each pixel.

conjectured that the performance of loopy belief prop agation on the Turbo structure was a special case of a more general phenomenon: We believe there are general undiscovered theorems about the performance of belief propagation on loopy DAGs. These theo rems, which may have directly to do with coding or decoding will show that in some sense belief propagation "converges with high ...

Fractional Stereo Matching Using Expectation-Maximization Wei Xiong, Hin Shun Chung,Student Member, IEEE, and Jiaya Jia,Member, IEEE Abstract—In our fractional stereo matching problem, a foreground object with a fractional boundary is blended with a background

using a dual layer loopy belief propagation; a coarse-to-ﬁne matching scheme is further adapted which can both speed up matching and obtain a better solution.

Protein structures play key roles in determining protein functions, activities, stability and subcellular localization. However, it is extremely time-consuming and expensive to determine experimentally the structures for millions of proteins using current techniques.

Belief propagation, also known as sum-product message passing, is a message-passing algorithm for performing inference on graphical models, such as Bayesian networks and Markov random fields.

than max-product and sum-product loopy belief propagation. 1 Introduction Graphical models allow expert modeling of com- plex relations and interactions between random variables. Since a graphical model with given pa-rameters denes a probability distribution, it can be used to reconstruct values for unobserved vari-ables. Themarginalinference problemistocom-pute the posterior marginal ...

Face Recognition in Multi-Camera Surveillance Videos Le An, Bir Bhanu, Songfan Yang Center for Research in Intelligent Systems, University of California, Riverside

We propose a novel energy function and use dual-layer loopy belief propagation to minimize it where the correspondence, the feature scale and rotation parameters are solved simultaneously. Our ...

We derive sufficient conditions for the uniqueness of loopy belief propagation fixed points. These conditions depend on both the structure of the graph and the strength of the potentials and naturally extend those for convexity of the Bethe free energy.

A dualmedia filter consists of a layer of anthracite coal above a layer of fine sand. The upper layer of coal traps most of the large floc, and the finer sand grains in the lower layer trap smaller impurities.

The dual-layer loopy belief propagation (Liu et al., 2011b) is utilized to minimize the energy function Eq. (1) . The guidance from higher-level is set as the form of message in optimization.

Dual-layer belief propagation u v Update within u plane ... Motion estimation for grouped contours • Gaussian MRF (GMRF) within a boundary fragment 𝜑 ;𝑏 = exp − − − −1 exp− 1 2𝜎2 −, +1 2 𝑛𝑖−1 =1 𝑛𝑖 =1 • The motions of two end edgelets are similar if they are grouped together 𝜙𝐕,,𝐕𝑆, = 1 if 𝑆, =, exp− 1 2𝜎2 𝐕, −𝐕𝑆, 2 ...

We propose a novel energy function and use dual-layer loopy belief propagation to minimize it where the correspondence, the feature scale and rotation parameters are solved simultaneously. Our method is effective and produces generally better results.

SIFTFlow [14] uses a dual-layer loopy belief propagation to ﬁnd correspondences between SIFT features. FullFlow [8] presents a global optimization algorithm using message passing and reduces the computational complexity of message update loop from quadratic to linear. SPM-BP [13]nte- grates message passing with PatchMatch-based particle generation algorithm. The key to applying BP to optical ...

than max-product and sum-product loopy belief propagation. 1 Introduction Graphical models allow expert modeling of com- plex relations and interactions between random variables. Since a graphical model with given pa-rameters deﬁnes a probability distribution, it can be used to reconstruct values for unobserved vari-ables. The marginal inference problem is to com-pute the posterior marginal ...

We propose a novel energy function and use dual-layer loopy belief propagation to minimize it where the correspondence, the feature scale and rotation parameters are solved simultaneously. Our ...

While loopy belief propagation is the canonical message-passing inference algorithm, many variations have been created to address some of its shortcomings. Some varia-tions modify the inference objective to make belief prop-agation a convex optimization, such as tree-reweighted ...

The dual-layer loopy belief propagation (Liu et al., 2011b) is utilized to minimize the energy function Eq. (1) . The guidance from higher-level is set as the form of message in optimization.

Introduction to Loopy Belief Propagation. Belief Propagation What is BP? I Belief Propagation is a dynamic programming approach to answering conditional probability queries in a graphical model. I Given some subset of the graph as evidence nodes (observed variables E), compute conditional probabilities on the rest of the graph (hidden variables X). I BP gives exact marginals when the graph is ...

Shreya Kadambi Deep Learning Software Autonomous . View Shreya Kadambi's profile on LinkedIn, the world's largest professional community. Shreya has 6 jobs listed on their profile.

We use a dual-layer loopy belief propagation as the base algorithm to optimize the objective function. Different from the usual formulation of optical flow [6], the smoothness term in the above equation is decoupled, which allows us to separate the horizontal flow u(p) from the vertical flow v(p) in message passing, as suggested by [7]. As a result, the complexity of the algorithm is reduced ...

we adopt the dual-layer loopy belief propagation [9] and the complexity of the algorithm is reduced from O(L 4 ) to O(L 2 ) per iteration compared with the conventional belief

Loopy Belief Propagation in Image-Based Rendering Dana Sharon Department of Computer Science University of British Columbia Abstract Belief propagation (BP) is a local-message passing

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## 3D Graph Neural Networks for RGBD Semantic Segmentation

3D Graph Neural Networks for RGBD Semantic Segmentation ... layer, respectively. Note that these update functions spec-ify a propagation model of information inside the graph. It is also possible to incorporate more information from the graph with different types of edges using multiple M. Inference is performed by executing the above propaga-tion model for a certain number of steps. The ...