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INCORPORATION OF UNRELIABLE INFORMATION INTO PHOTOGRAMMETRIC RECONSTRUCTION FOR RECOVERY OF SCALE USING NON-PARAMETRIC BELIEF PROPAGATION
Sensor Fusion Belief Propagation Photogrammetry Barometer GNSS
2016/11/11
The creation of large photogrammetric models often encounter several difficulties in regards to geometric accuracy, scale and geolocation, especially when not using control points. Geometric accuracy ...
Structured Learning for Taxonomy Induction with Belief Propagation
Structured Learning Taxonomy Induction Belief Propagation
2016/1/22
We present a structured learning approach to inducing hypernym taxonomies using a probabilistic graphical model formulation.Our model incorporates heterogeneous re-lational evidence about both hyperny...
Scaling MCMC Inference and Belief Propagation to Large, Dense Graphical Models
machine learning graphical models
2014/12/18
With the physical constraints of semiconductor-based electronics becoming increasingly limiting in the past decade, single-core CPUs have given way to multi-core and distributed computing platforms. A...
Adaptive Image Deblurring via Tanner Graph Representation and Belief Propagation
Adaptive Representation Belief Propagation
2013/7/24
Image deblurring aims to counteract the effect of point spread function (PSF) inimperfect image formation system. Conventional solutions include frequency-domainapproaches (e.g. Wiener filtering...
该文首先通过仿真证明了LDPC(Low Density Parity Check code)在分组衰落信道下,以通常的Belief-propagation算法译码,具有较好性能.然后基于算法的特殊迭代特性,提出分组衰落信道下,在每一迭代步骤中结合信道估计的改进的Belief-propagation算法.仿真证明,该算法可以有效地减少译码迭代次数.