Dual Layer Loopy Belief Propagation

Loopy Belief Propagation: Convergence and Effects of

to the operations of belief propagation. This allows us to derive conditions for the convergence of traditional loopy belief propagation, and bounds on the distance between any pair of BP fixed points (Sections 5.1–5.2), and these results are easily extended to many approximate forms of BP (Section 5.3). If the errors introduced are

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Loopy Belief Propagation CNBC

What if the graph is not a tree Several alternative methods: Gibbs sampling Expectation Maximization Variational methods Elimination Algorithm Junction-Tree algorithm Loopy Belief Propagation Elimination Algorithm Inferring P(x1): Loopy Belief Propagation Just apply BP rules in spite of loops In each iteration, each node sends all messages in

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SIFT Flow: Dense Correspondence across Scenes and its

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].

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Dense Descriptors for Optical Flow Estimation: A

displacement and smoothness terms, dual-layer loopy belief propagation is utilized for optimization. The proposed technique is proven to be useful in video retrieval, motion prediction from a single image, image registration and face recognition. However, a more comprehensive evaluation of the method for optical flow estimation is missing. In

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SD-SEM: sparse-dense correspondence for 3D reconstruction

The flow is then extracted by using a dual-layer loopy belief propagation algorithm. Fig. 4 shows the factor graph suggested by Liu et al. (2011) for optimizing the energy functional of dense matching problem. By using a coarse-to-fine (multi-resolution) matching scheme, one is able to reduce the computational complexity and hence the

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SD-SEM: sparse-dense correspondence for 3D reconstruction

The flow is then extracted by using a dual-layer loopy belief propagation algorithm. Fig. 4 shows the factor graph suggested by Liu et al. (2011) for optimizing the energy functional of dense matching problem. By using a coarse-to-fine (multi-resolution) matching scheme, one is able to reduce the computational complexity and hence the

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The Pennsylvania State University The Graduate School

The Pennsylvania State University The Graduate School College of Engineering ACCELERATION OF MONOCULAR DEPTH EXTRACTION FOR IMAGES A Thesis in Computer Science and Engineering by Anusha Chandrashekhar c 2014 Anusha Chandrashekhar Submitted in Partial Ful llment of the Requirements for the Degree of Master of Science December 2014. The thesis of Anusha

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Image Registration Algorithm based on Regular Sparse

dual-layer loopy belief propagation in the optimization [9]. C. Topology term Although the smoothness term improves the overall regis-tration performances, the cost function consisting of the data and smoothness terms may yield inconsistent results (e.g., one

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Dense image correspondence under large appearance variations

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

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Scale-Space SIFT Flow

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. There is no scale factor in Eqn. (1), while in many dense feature matching applications, images are at different scales. In SIFT flow, dense SIFT feature computed in fixed grids and

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Belief propagation Wikipedia

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.It calculates the marginal distribution for each unobserved node (or variable), conditional on any observed nodes (or variables). Belief propagation is commonly used in artificial intelligence and

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ASPECT RATIO SIMILARITY (ARS) FOR IMAGE RETARGETING

[15], which is a dual-layer loopy belief propagation based al-gorithm and utilize a coarse-to-fine scheme to speed up the optimization. The geometric change estimation results are shown in Fig. 2. The column (b) is the retargeted images and the column (c) is the visualized geometric change estimation

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DENSE IMAGE CORRESPONDENCE UNDER LARGE APPEARANCE VARIATIONS

a novel energy function and use dual-layer loopy belief prop-agation to minimize it where the correspondence, the feature scale and rotation parameters are solved simultaneously. Our method is effective and produces generally better results. Index Terms— image registration, image matching, im-age motion analysis, SIFT Flow, belief propagation

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A Loopy Belief Propagation approach for robust background

A Loopy Belief Propagation approach for robust background estimation Conference Paper in Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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Dense image correspondence under large appearance

We apply the idea in a flow-based matching framework and utilize the best feature sample for each pixel to determine the flow field. 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

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Learning discriminated and correlated patches for multi

The method makes use of the effective dual-layer loopy belief propagation and a coarse-to-fine matching scheme to obtain dense pixel-wise correspondences between two images. To illustrate, Fig. 5 shows an example, where dense feature points (Middle) are extracted from two motorbike images (Top) of different viewpoints.

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Dual Decomposition Inference for Graphical Models over Strings

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

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Face Recognition in Multi-Camera Surveillance Videos

Face Recognition in Multi-Camera Surveillance Videos Le An, Bir Bhanu, Songfan Yang Center for Research in Intelligent Systems, University of California, Riverside [email protected], [email protected], [email protected] Abstract Recognizing faces in surveillance videos becomes dif®cult due to the poor quality of the probe data in

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arXiv:1906.07880v1 [cs.CL] 19 Jun 2019

variational inference or loopy belief propagation to approximately find the highest-scoring parse graph. Both algorithms are iterative inference al-gorithms and we show that they can be unfolded as recurrent layers of a neural network with each layer representing the computation in one itera-tion of the algorithms. In this way, we can con-

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Dependency parsing by belief propagation

We formulate dependency parsing as a graphical model with the novel ingredient of global constraints. We show how to apply loopy belief propagation (BP), a simple and effective tool for approximate learning and inference. As a parsing algorithm, BP is both asymptotically and empirically efficient.

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Loopy belief propagation for approximate inference

Joris Mooij,Hilbert J. Kappen, Validity estimates for loopy Belief Propagation on binary real-world networks, Proceedings of the 17th International Conference on Neural Information Processing Systems, p.945-952, December 01, 2004, Vancouver, British Columbia, Canada

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Facial Expression Recognition Using Emotion Avatar Image

The dual-layer loopy belief propagation is used as the base algorithm to optimize the objective function. Fig. 2 shows the factor graph of the model. Then, a coarse-to-fine SIFT flow matching scheme is adopted to improve the speed and the matching result. Fig. 3 contains two frames (frame 1 and frame 30) in

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Doctor Penguin

14/11/2019· Computer science, Dual Layer Loopy Belief Propagation Network (DLBPN), Keyframes, SIFT flow, Uniform sampling, Video summarization. 14 Nov 2019. 2019-11-14; articles; Weekly Summary Receive a weekly summary and discussion of the top papers of

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Kok-Lim Low NUS Computing

4: System and Method for Animating Real Objects with Projected Images. Gregory F. Welch, Kok-Lim Low, Ramesh Raskar. U.S. Patent #7,068,274, June 27, 2006. 3: System and Method for Registering Multiple Images with Three-Dimensional Objects.

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Abnormal Crowd Motion Behaviour Detection based on SIFT Flow

International Journal of Signal Processing, Image Processing and Pattern Recognition Vol.9, No.1 (2016), pp.289-302 dx.doi/10.14257/ijsip.2016.9.1.28

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