Fast Face Localisation Using AdaBoost Algorithm and Identification with Matrix Decomposition Methods

2011 ◽  
pp. 242-250 ◽  
Author(s):  
Tomasz Marciniak ◽  
Szymon Drgas ◽  
Damian Cetnarowicz
Keyword(s):  
Matrix Decomposition ◽  
Decomposition Methods ◽  
Adaboost Algorithm

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 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.

Comparison of Matrix Dimensionality Reduction Methods in Uncovering Latent Structures in the Data

2010 ◽  
Vol 09 (01) ◽  
pp. 81-92 ◽  
Author(s):  
Ch. Aswani Kumar ◽  
Ramaraj Palanisamy
Keyword(s):  
Dimensionality Reduction ◽  
Time Series Data ◽  
Matrix Decomposition ◽  
Decomposition Methods ◽  
Singular Value ◽  
Series Data ◽  
High Dimensional ◽  
Reduction Methods ◽  
Latent Structures ◽  
Value Decomposition

Matrix decomposition methods: Singular Value Decomposition (SVD) and Semi Discrete Decomposition (SDD) are proved to be successful in dimensionality reduction. However, to the best of our knowledge, no empirical results are presented and no comparison between these methods is done to uncover latent structures in the data. In this paper, we present how these methods can be used to identify and visualise latent structures in the time series data. Results on a high dimensional dataset demonstrate that SVD is more successful in uncovering the latent structures.

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