Analysis of the effect of motion estimation error on the motion adaptive spatial filter and its application

1996 ◽  
Vol 35 (7) ◽  
pp. 2045 ◽  
Author(s):  
Sang‐Yeon Kim
Keyword(s):  
Motion Estimation ◽  
Estimation Error ◽  
Spatial Filter ◽  
Adaptive Spatial Filter

An Adaptive Spatial Filter for User-Independent Single Trial Detection of Event-Related Potentials

2015 ◽  
Vol 62 (7) ◽  
pp. 1696-1705 ◽  
Author(s):  
Hendrik Woehrle ◽  
Mario M. Krell ◽  
Sirko Straube ◽  
Su Kyoung Kim ◽  
Elsa A. Kirchner ◽  
...  
Keyword(s):  
Event Related Potentials ◽  
Spatial Filter ◽  
Single Trial ◽  
Related Potentials ◽  
Adaptive Spatial Filter

Effect of Domain Size on Fluid–Particle Statistics in Homogeneous, Gravity-Driven, Cluster-Induced Turbulence

2015 ◽  
Vol 138 (4) ◽  
Author(s):  
Jesse Capecelatro ◽  
Olivier Desjardins ◽  
Rodney O. Fox
Keyword(s):  
Volume Fraction ◽  
Finite Size ◽  
Domain Size ◽  
Spatial Filter ◽  
High Mass ◽  
Inertial Particles ◽  
Spatially Correlated ◽  
Gravity Driven ◽  
Adaptive Spatial Filter ◽  
Particle Statistics

Scaling of volume fraction and velocity fluctuations with domain size is investigated for high-mass-loading suspensions of finite-size inertial particles subject to gravity. Results from highly resolved Euler–Lagrange simulations are evaluated via an adaptive spatial filter with an averaging volume that varies with the local particle concentration. This filter enables the instantaneous particle velocity to be decomposed into a spatially correlated contribution used in defining the particle-phase turbulent kinetic energy (TKE), and a spatially uncorrelated contribution used in defining the granular temperature. The total granular energy is found to grow nearly linearly with the domain size, while the balance between the separate contributions remains approximately constant.

scholarly journals A New Adaptive Spatial Filtering Method in the Wavelet Domain for Medical Images

2020 ◽  
Vol 10 (16) ◽  
pp. 5693
Author(s):  
Maria Simona Răboacă ◽  
Cătălin Dumitrescu ◽  
Constantin Filote ◽  
Ioana Manta
Keyword(s):  
Spatial Filtering ◽  
Threshold Value ◽  
Spatial Filter ◽  
Wavelet Domain ◽  
Wavelet Coefficients ◽  
Local Statistics ◽  
Filtering Method ◽  
New Methods ◽  
Computational Resources ◽  
Adaptive Spatial Filter

Although there are many methods in the literature to eliminate noise from images, finding new methods remains a challenge in the field and, despite the complexity of existing methods, many of the methods do not reach a sufficient level of applicability, most often due to the relatively high calculation time. In addition, most existing methods perform well when the processed image is adapted to the algorithm, but otherwise fail or results in significant artifacts. The context of eliminating noise from images is similar to that of improving images and for this reason some notions necessary to understand the proposed method will be repeated. An adaptive spatial filter in the wavelet domain is proposed by soft truncation of the wavelet coefficients with threshold value adapted to the local statistics of the image and correction based on the hierarchical correlation map. The filter exploits, in a new way, both the inter-band and the bandwidth dependence of the wavelet coefficients, considering the minimization of computational resources.

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