Reformulating the least-square source localization problem with contracted distances

2009 ◽  
Author(s):  
Giuseppe Destino ◽  
Giuseppe Abreu
Keyword(s):  
Source Localization ◽  
Least Square ◽  
Localization Problem

Iterative Minimization Schemes for Solving the Single Source Localization Problem

2008 ◽  
Vol 19 (3) ◽  
pp. 1397-1416 ◽  
Author(s):  
Amir Beck ◽  
Marc Teboulle ◽  
Zahar Chikishev
Keyword(s):  
Source Localization ◽  
Single Source ◽  
Localization Problem ◽  
Iterative Minimization

scholarly journals Systematic Regularization of Linear Inverse Solutions of the EEG Source Localization Problem

NeuroImage ◽  
2002 ◽  
Vol 17 (1) ◽  
pp. 287-301 ◽  
Author(s):  
Christophe Phillips ◽  
Michael D. Rugg ◽  
Karl J. Friston
Keyword(s):  
Source Localization ◽  
Localization Problem ◽  
Inverse Solutions ◽  
Eeg Source Localization

Application Analysis of Compressive Sensing/Sampling Theory on Source Localization

2012 ◽  
Vol 239-240 ◽  
pp. 1409-1412
Author(s):  
Xue Bing Han ◽  
Chun Hui Qiu ◽  
Zhao Jun Jiang
Keyword(s):  
Compressive Sensing ◽  
Source Localization ◽  
Sampling Theory ◽  
Restricted Isometry Property ◽  
Necessary Condition ◽  
Localization Problem ◽  
Sensing Matrix ◽  
Application Analysis

In this paper, we consider the source localization problem with Compressive Sensing/Sampling (CS) Theory. CS Theory asserts one can reconstruct sparse or compressible signals from a very limited number of measurements. A necessary condition relies on properties of the sensing matrix such as the restricted isometry property (RIP). This paper explains why sparse construction can be used in source localization with RIP conception.

scholarly journals Efficient Iris Localization via Optimization Model

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Qi Wang ◽  
Zhipeng Liu ◽  
Shu Tong ◽  
Yuqi Yang ◽  
Xiangde Zhang
Keyword(s):  
Optimization Model ◽  
Iris Recognition ◽  
Outer Boundary ◽  
Descent Method ◽  
Least Square ◽  
Experimental Result ◽  
Localization Problem ◽  
Localization Algorithm ◽  
Iris Localization ◽  
Supervised Descent Method

Iris localization is one of the most important processes in iris recognition. Because of different kinds of noises in iris image, the localization result may be wrong. Besides this, localization process is time-consuming. To solve these problems, this paper develops an efficient iris localization algorithm via optimization model. Firstly, the localization problem is modeled by an optimization model. Then SIFT feature is selected to represent the characteristic information of iris outer boundary and eyelid for localization. And SDM (Supervised Descent Method) algorithm is employed to solve the final points of outer boundary and eyelids. Finally, IRLS (Iterative Reweighted Least-Square) is used to obtain the parameters of outer boundary and upper and lower eyelids. Experimental result indicates that the proposed algorithm is efficient and effective.

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