scholarly journals Customized depolarization spatial patterns with dynamic retardance functions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
David Marco ◽  
Guadalupe López-Morales ◽  
María del Mar Sánchez-López ◽  
Ángel Lizana ◽  
Ignacio Moreno ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Detection System ◽  
Temporal Integration ◽  
Matrix Decomposition ◽  
Decomposition Methods ◽  
Time Dependent ◽  
Light Modulator ◽  
Dynamical Time ◽  
Depolarization Effect ◽  
Spatially Variant

AbstractIn this work we demonstrate customized depolarization spatial patterns by imaging a dynamical time-dependent pixelated retarder. A proof-of-concept of the proposed method is presented, where a liquid–crystal spatial light modulator is used as a spatial retarder that emulates a controlled spatially variant depolarizing sample by addressing a time-dependent phase pattern. We apply an imaging Mueller polarimetric system based on a polarization camera to verify the effective depolarization effect. Experimental validation is provided by temporal integration on the detection system. The effective depolarizance results are fully described within a simple graphical approach which agrees with standard Mueller matrix decomposition methods. The potential of the method is discussed by means of three practical cases, which include non-reported depolarization spatial patterns, including exotic structures as a spirally shaped depolarization pattern.

scholarly journals Customized Depolarization Spatial Patterns With Dynamic Retardance Functions

2021 ◽  
Author(s):  
David Marco ◽  
Guadalupe López-Morales ◽  
María Mar Sánchez-López ◽  
Ángel Lizana ◽  
Ignacio Moreno ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Detection System ◽  
Temporal Integration ◽  
Matrix Decomposition ◽  
Decomposition Methods ◽  
Time Dependent ◽  
Phase Mask ◽  
Light Modulator ◽  
Dynamical Time ◽  
Spatially Variant

Abstract In this work we demonstrate customized depolarization spatial patterns by imaging a dynamical time-dependent pixelated retarder. A proof-of-concept of the proposed method is presented, where a liquid-crystal spatial light modulator is used as a spatial retarder that emulates a controlled spatially variant depolarizing sample by addressing a time-dependent phase mask. We apply an imaging Mueller polarimetric system based on a polarization camera to verify the effective depolarization effect. Experimental validation is provided by temporal integration on the detection system. The effective depolarizance results are fully described within a simple graphical approach which agrees with standard Mueller matrix decomposition methods. The potential of the method is discussed by means of three practical cases, which include non-reported depolarization spatial patterns, including exotic structures as a spirally shaped depolarization pattern.

scholarly journals Diffuse Optical Imaging Using Decomposition Methods

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Binlin Wu ◽  
M. Alrubaiee ◽  
W. Cai ◽  
M. Xu ◽  
S. K. Gayen
Keyword(s):  
Experimental Data ◽  
Optical Imaging ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Principal Component ◽  
Matrix Decomposition ◽  
Decomposition Methods ◽  
Component Analysis ◽  
Diffuse Optical Imaging

Diffuse optical imaging (DOI) for detecting and locating targets in a highly scattering turbid medium is treated as a blind source separation (BSS) problem. Three matrix decomposition methods, independent component analysis (ICA), principal component analysis (PCA), and nonnegative matrix factorization (NMF) were used to study the DOI problem. The efficacy of resulting approaches was evaluated and compared using simulated and experimental data. Samples used in the experiments included Intralipid-10% or Intralipid-20% suspension in water as the medium with absorptive or scattering targets embedded.

scholarly journals Peak-at-end rule: adaptive mechanism predicts time-dependent decision weighting

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Ryuto Yashiro ◽  
Isamu Motoyoshi
Keyword(s):  
Decision Process ◽  
Temporal Dynamics ◽  
Temporal Integration ◽  
Time Dependent ◽  
Adaptive Mechanism ◽  
Perceptual Decision Making ◽  
Estimation Task ◽  
Leaky Integrator ◽  
Sensory Evidence ◽  
The Brain

Abstract Humans make decisions under various natural circumstances, integrating multiple pieces of information that are distributed over space and time. Although psychophysical and physiological studies have investigated temporal dynamics underlying perceptual decision making, weighting profiles for inliers and outliers during temporal integration have yet to be fully investigated in most studies. Here, we examined the temporal weighting profile of a computational model characterized by a leaky integrator of sensory evidence. As a corollary of its leaky nature, the model predicts the recency effect and overweights outlying elements around the end of the stream. Moreover, we found that the model underweights outlying values occurring earlier in the stream (i.e., robust averaging). We also show that human observers exhibit exactly the same weighting profile in an average estimation task. These findings suggest that the adaptive decision process in the brain results in the time-dependent decision weighting, the “peak-at-end” rule, rather than the peak-end rule in behavioral economics.

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