scholarly journals A novel objective function minimization for sparse spatial filters

2014 ◽  
Author(s):  
Ibrahim Onaran ◽  
N. Firat Ince ◽  
A. Enis Cetin
Keyword(s):  
Objective Function ◽  
Function Minimization ◽  
Spatial Filters

A New Image Demising Method Based on Partial Differential Equations

2013 ◽  
Vol 443 ◽  
pp. 22-26
Author(s):  
Yong Xing Lin ◽  
Xiao Yan Xu ◽  
Xian Dong Zhang
Keyword(s):  
Image Processing ◽  
Partial Differential Equations ◽  
Differential Equations ◽  
Diffusion Equation ◽  
Objective Function ◽  
Thermal Diffusion ◽  
Minimization Problem ◽  
Iterative Process ◽  
Function Minimization ◽  
Partial Differential

In the paper, we discuss the image demising models, based on partial differential equations. It is through the use of the concept of variations in the calculus of the objective function minimization problem, defines the image processing tasks. The results show that the model expands 2d thermal diffusion equation. Therefore, it is easy to get solution is to use a simple iterative process.

scholarly journals Correlation-based common spatial pattern (CCSP): A novel extension of CSP for classification of motor imagery signal

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248511
Author(s):  
Khatereh Darvish ghanbar ◽  
Tohid Yousefi Rezaii ◽  
Ali Farzamnia ◽  
Ismail Saad
Keyword(s):  
Spatial Pattern ◽  
Objective Function ◽  
Prior Information ◽  
Time Correlation ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Common Spatial Pattern ◽  
Spatial Filters ◽  
Discriminate Analysis

Common spatial pattern (CSP) is shown to be an effective pre-processing algorithm in order to discriminate different classes of motor-based EEG signals by obtaining suitable spatial filters. The performance of these filters can be improved by regularized CSP, in which available prior information is added in terms of regularization terms into the objective function of conventional CSP. Variety of prior information can be used in this way. In this paper, we used time correlation between different classes of EEG signal as the prior information, which is clarified similarity between different classes of signal for regularizing CSP. Furthermore, the proposed objective function can be easily extended to more than two-class problems. We used three different standard datasets to evaluate the performance of the proposed method. Correlation-based CSP (CCSP) outperformed original CSP as well as the existing regularized CSP, Principle Component Cnalysis (PCA) and Fisher Discriminate Analysis (FDA) in both two-class and multi-class scenarios. The simulation results showed that the proposed method outperformed conventional CSP by 6.9% in 2-class and 2.23% in multi-class problem in term of mean classification accuracy.

Comparison of Deterministic and Linear Cosine Spatial Filtering of Transmission Electron Micrographs

1972 ◽  
Vol 30 ◽  
pp. 596-597
Author(s):  
David A. Ansley
Keyword(s):  
Spherical Aberration ◽  
Focal Length ◽  
Chromatic Aberration ◽  
Spatial Filter ◽  
Operating Conditions ◽  
Objective Lens ◽  
List Type ◽  
Spatial Filters ◽  
Transmission Electron ◽  
Wide Range

The coherence of the electron flux of a transmission electron microscope (TEM) limits the direct application of deconvolution techniques which have been used successfully on unmanned spacecraft programs. The theory assumes noncoherent illumination. Deconvolution of a TEM micrograph will, therefore, in general produce spurious detail rather than improved resolution.A primary goal of our research is to study the performance of several types of linear spatial filters as a function of specimen contrast, phase, and coherence. We have, therefore, developed a one-dimensional analysis and plotting program to simulate a wide 'range of operating conditions of the TEM, including adjustment of the:(1) Specimen amplitude, phase, and separation(2) Illumination wavelength, half-angle, and tilt(3) Objective lens focal length and aperture width(4) Spherical aberration, defocus, and chromatic aberration focus shift(5) Detector gamma, additive, and multiplicative noise constants(6) Type of spatial filter: linear cosine, linear sine, or deterministic

A light optical diffractometer for electron microscopical images operating in line

1978 ◽  
Vol 36 (1) ◽  
pp. 86-87
Author(s):  
P. Bonhomme ◽  
A. Beorchia
Keyword(s):  
Electron Microscopy ◽  
Electron Transfer ◽  
Electron Microscope ◽  
Image Converter ◽  
Coherent Light ◽  
Spatial Filters ◽  
Electron Image ◽  
Electron Microscopical ◽  
Working Parameters ◽  
Amorphous Part

We have already described (1.2.3) a device using a pockel's effect light valve as a microscopical electron image converter. This converter can be read out with incoherent or coherent light. In the last case we can set in line with the converter an optical diffractometer. Now, electron microscopy developments have pointed out different advantages of diffractometry. Indeed diffractogram of an image of a thin amorphous part of a specimen gives information about electron transfer function and a single look at a diffractogram informs on focus, drift, residual astigmatism, and after standardizing, on periods resolved (4.5.6). These informations are obvious from diffractogram but are usualy obtained from a micrograph, so that a correction of electron microscope parameters cannot be realized before recording the micrograph. Diffractometer allows also processing of images by setting spatial filters in diffractogram plane (7) or by reconstruction of Fraunhofer image (8). Using Electrotitus read out with coherent light and fitted to a diffractometer; all these possibilities may be realized in pseudoreal time, so that working parameters may be optimally adjusted before recording a micrograph or before processing an image.

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