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 fieldsIt 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...

which double computation and memory consumption Pos- layer respectively Note that these update functions spec-ify a propagation model of information inside the graph It to approximate inference in general MRFs/CRFs is loopy belief propagation BP 31 8 The propagation process is...

International Journal of Signal Processing Image Processing and Pattern Recognition Vol9 No1 2016 pp289-302 dxdoiorg/1014257/ijsip20169128...

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...

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...

a loopy belief propagation LBP algorithm is used 24 25 As a conditional probability model LBP can be considered as a generalization of the Markov chain and can effectively describe the correlation of all the nodes/pixels in the ﬁeld It is based on the Markov random ﬁeld MRF which assumes that the...

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...

The Pennsylvania State University The Graduate School College of Engineering ACCELERATION OF MONOCULAR DEPTH EXTRACTION FOR IMAGES A Thesis in Computer Science and...

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...

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 The horizontal flow and vertical flow are separated in the message passing since both smoothness term and guidance term are decoupled which can improve...

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...

Selenium Rectifier Layer THE USE OF SELENIUM VALVES IN RECTIFIERS Philips 2014114 ensp enspTHE USE OF SELENIUM VALVES IN RECTIFIERS by D MDUINKER 621314634 The construction and operatien ofblockinglayer valves in particular the selenium valves manufactured by Philips hasalready been dealt with inthis its appliion in different rectifier connections...

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...

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...

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-...

The A junction graph is a two layer region graph with corresponding loopy iterative scaling algorithm is now large regions called cliques and their children called based on the generalized belief propagation algorithms 5 as described in Yedidia et al 2002...

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 24 Dual Decomposition Inference In section 4 we will present a dual decomposition...

Fractional Stereo Matching Using Expectation-Maximization Wei Xiong Hin Shun Chung Student Member IEEE and Jiaya Jia Member IEEE our method assumes a dual-layer model for the foreground and background and establishes the color Loopy Belief Propagation LBP 5 and Graph Cuts 1 are two popular methods for minimizing the Gibbs...

A Brunton C Shu and G Roth Belief propagation on the gpu fo r stereo vision 3rd Canadian Conference on Computer and Robot Vision 2006 J Coughlan and H Shen Dynamic Quantization for Belief Propagation in Sparse Spac Computer Vision and Image Understanding CVIU...

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