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Scale-Space SIFT Flow - CiteSeerX

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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A Comparison of Loopy Belief Propagation and Dual ...

dual decomposition integrated ccg supertagging loopy belief propagation structured natural language processing problem many bad par gold po tag first empirical comparison dual decomposition approach upper bound search space belief propagation ccg parser distinct model automatic part-of-speeoch tag single model labelled dependency f-measure ...

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Belief Propagation in Conditional RBMs for Structured ...

Approximate inference methods, such as mean field (MF) and belief propagation (BP), can be employed as inference routines in learning as well as for making predictions after the CRBM has been learned (Welling and Teh, 2003; Yasuda and Tanaka, 2009).Although loopy BP usually provides a better approximation of marginals than MF (Murphy et al., 1999), it was found to be slow on CRBMs for ...

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Dual Decomposition Inference for Graphical Models over Strings

than max-product and sum-product loopy belief propagation. 1 Introduction ... observed word at layer 3 has a latent underlying form at layer 2, which is a deterministic concatenation of latent morphemes at ... of Expectation Propagation (Minka, 2001). 2.4 Dual Decomposition Inference In section 4, we will present a dual decomposition

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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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Max-Product Loopy Belief Propagation_diemeng1119 …

Max-Product Loopy Belief Propagationbelief propagation。machine learningJ. Pearl。,(conditional marginal probability)。,,

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Introduction to Loopy Belief Propagation

Belief Propagation Notation I Define λ Y (x) as the message to X from a child node Y, indicating Y's opinion of how likely it is that X = x. I If X is observed (X ∈ E), allow a message to itself: λ X(x). I Define π X(u) as the message to X from its parent U, used to reweight the distribution of X given that U = u. I Keep passing messages around until the beliefs converge.

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Facial Expression Recognition Using Emotion Avatar Image

Oct 01, 1997· 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 …

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SIFT:() - -

2.dual-layer loopy belief propagation。() 3. 1.,,。(coarse-to-fine SIFT flow matiching scheme)

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Generalized Deformable Spatial Pyramid: Geometry ...

the descriptors via dual-layered loopy belief propagation. The local gradient information and pixel-level regulariza-tion enable fine matching even across different scenes or objects. However, the lack of the consideration of scale and rotation confines its scope of matching scenarios. There have been several extensions of SIFT Flow to

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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).

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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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US8644599B2 - Method and apparatus for spawning specialist ...

A new belief propagation architecture is presented that may be comprised of three or more types of subnets: 1) a deep belief propagation subnet that is responsible for gesture recognition, and 2) a second type of belief propagation subnet that is responsible for facial expression detection and recognition, and 3) a monitor subnet that manages ...

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Tensor Networks contraction and the Belief Propagation ...

Tensor Networks contraction and the Belief Propagation algorithm R. Alkabetz 1and I. Arad 1Department of Physics, Technion, 3200003 Haifa, Israel (Dated: August 12, 2020) Belief Propagation is a well-studied message-passing algorithm that runs over graphical models and can be used for approximate inference and approximation of local marginals.

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A Tutorial Introduction to Belief Propagation

C. Yanover and Y. Weiss. "Finding the M Most Probable Configurations using Loopy Belief Propagation." NIPS 2003. Speed-ups: B. Potetz and T.S. Lee. "Efficient belief propagation for higher-order cliques using linear constraint nodes." Comput. Vis. Image Understanding, 112(1):39-54. 2008.

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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.

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Computational Models of Belief Propagation

and explore ways to improve the loopy belief propagation. We propose a new approximate inference method called the 2-Pass loopy belief propagation. Our idea of applying a 2-Pass control to the loopy belief propagation was born from understanding the effects of the 2-Pass evidence propagation of the join tree algorithm. We investigate

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Applications of Belief Propagation on Loopy Graphs

Applications of Belief Propagation on Loopy Graphs Jennah Gosciak March 9, 2019 1Introduction Belief propagation is an algorithm used for inference on graphical models, often with ap-plications in arti cial intelligence and information theory. Graphical models are a useful tool when analyzing complex probability distributions.

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Loopy Belief Propagation in Image-Based Rendering

2 Belief Propagation Belief propagation (BP) is a local-message passing technique that solves inference problems on graphical models. It has been shown [12] to produce exact in-ferences on singly connected graphs. Moreover, em-pirical studies [1, 14, 10] have shown that, although beliefs don't always converge on loopy graphs, when

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Dual Decomposition Inference for Graphical Models over Strings

2.3 Finite-State Belief Propagation BP iteratively updates messages between factors and variables. Each message is a vector whose el-ements score the possible values of a variable. Murphy et al. (1999) discusses BP on cyclic ( loopy ) graphs. For pedagogical reasons, sup-pose momentarily that all factors have degree 2 (this loses no power).

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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.

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428 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND …

considering labeling smoothness [7], [9], [16], [18]. Loopy Belief Propagation (LBP) [5] and Graph Cuts [1] are two popular methods for minimizing the Gibbs energy. Sun et al. [18] introduced a MAP estimation of disparity values. This method is further improved in [16] by adding the symmetry constraint and integrating color segmentation

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Belief Propagation Neural Networks | DeepAI

To bridge this gap, we introduce belief propagation neural networks (BPNNs), a class of parameterized operators that operate on factor graphs and generalize Belief Propagation (BP). In its strictest form, a BPNN layer (BPNN-D) is a learned iterative operator that provably maintains many of the desirable properties of BP for any choice of the ...

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Hybrid Loopy Belief Propagation

Hybrid Loopy Belief Propagation Changhe Yuan Department of Computer Science and Engineering Mississippi State University Mississippi State, MS 39762 [email protected] Marek J. Druzdzel Decision Systems Laboratory School of Information Sciences University of Pittsburgh Pittsburgh, PA 15260 [email protected] Abstract

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Belief Propagation()_ICAOYS …

Belief Propagation() Belief Propagation,,,。。:Bayes Markov …

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Loopy belief propagation, Markov Random Field, stereo ...

40 iterations of loopy belief propagation; This gives us the following disparity map. Disparity map using loopy belief propagation. which is a much nicer disparity map than on our first attempt! The disparities are much smoother. We can make out the individual objects much better than before, especially the camera on the tripod in the background.

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Indoor Positioning Using Nonparametric Belief Propagation ...

Nonparametric belief propagation (NBP) is one of the best-known methods for cooperative localization in sensor networks. It is capable of providing information about location estimation with appropriate uncertainty and to accommodate non-Gaussian distance measurement errors. However, the accuracy of NBP is questionable in loopy networks.

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Linear vs Nonlinear Extreme Learning Machine for Spectral ...

combining ELM with loopy belief propagation (LBP). The original ELM is linear, and the nonlinear ELMs (or Kernel ELMs) are the improvement of linear ELM (LELM). However, based on lots of experiments and analysis, we found out that the LELM is a better choice than nonlinear ELM for spectral-spatial classification of HSI. Furthermore, we

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Book: Machine Learning: a Probabilistic Perspective - Data ...

Nov 15, 2017· Loopy BP vs mean field 783; 22.4 Extensions of belief propagation * 783. Generalized belief propagation 783; Convex belief propagation 785; 22.5 Expectation propagation 787. EP as a variational inference problem 788; Optimizing the EP objective using moment matching 789; EP for the clutter problem 791; LBP is a special case of EP 792

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libDAI: File List

Defines class BBP, which implements Back-Belief-Propagation bipgraph.h: Defines the BipartiteGraph class, which represents a bipartite graph bp.h: Defines class BP, which implements (Loopy) Belief Propagation bp_dual.h: Defines class BP_dual, which is used primarily by BBP cbp.h: Defines class CBP, which implements Conditioned Belief Propagation

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DENSE IMAGE CORRESPONDENCE UNDER LARGE …

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 1.