Fast Spatial Averaging Filter

2012 ◽  
Author(s):  
Chun-Ming Tsai ◽  
Zong-Mu Yeh
Keyword(s):  
Spatial Averaging ◽  
Averaging Filter

scholarly journals Conductivity image enhancement in MREIT using adaptively weighted spatial averaging filter

2014 ◽  
Vol 13 (1) ◽  
pp. 87
Author(s):  
Tong Oh ◽  
Hyung Kim ◽  
Woo Jeong ◽  
Hun Wi ◽  
Oh Kwon ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Spatial Averaging ◽  
Averaging Filter ◽  
Conductivity Image

Memory efficient and constant time 2D-recursive spatial averaging filter for embedded implementations

2016 ◽  
Author(s):  
Qifeng Gan ◽  
Lama Seoud ◽  
Houssem Ben Tahar ◽  
J.M. Pierre Langlois
Keyword(s):  
Constant Time ◽  
Spatial Averaging ◽  
Averaging Filter ◽  
Memory Efficient

scholarly journals Spatial averaging and apparent acceleration in inhomogeneous spaces

2011 ◽  
Vol 44 (2) ◽  
pp. 353-365 ◽  
Author(s):  
Antonio Enea Romano ◽  
Misao Sasaki
Keyword(s):  
Spatial Averaging ◽  
Apparent Acceleration

scholarly journals Spatial and Temporal Integration of Visual Motion Signals for Smooth Pursuit Eye Movements in Monkeys

2009 ◽  
Vol 102 (4) ◽  
pp. 2013-2025 ◽  
Author(s):  
Leslie C. Osborne ◽  
Stephen G. Lisberger
Keyword(s):  
Random Walk ◽  
Eye Movements ◽  
Smooth Pursuit ◽  
Visual Motion ◽  
Target Motion ◽  
Linear Filter ◽  
Smooth Pursuit Eye Movements ◽  
Spatial Averaging ◽  
Spatial Integration ◽  
Pursuit Eye Movements

To probe how the brain integrates visual motion signals to guide behavior, we analyzed the smooth pursuit eye movements evoked by target motion with a stochastic component. When each dot of a texture executed an independent random walk such that speed or direction varied across the spatial extent of the target, pursuit variance increased as a function of the variance of visual pattern motion. Noise in either target direction or speed increased the variance of both eye speed and direction, implying a common neural noise source for estimating target speed and direction. Spatial averaging was inefficient for targets with >20 dots. Together these data suggest that pursuit performance is limited by the properties of spatial averaging across a noisy population of sensory neurons rather than across the physical stimulus. When targets executed a spatially uniform random walk in time around a central direction of motion, an optimized linear filter that describes the transformation of target motion into eye motion accounted for ∼50% of the variance in pursuit. Filters had widths of ∼25 ms, much longer than the impulse response of the eye, and filter shape depended on both the range and correlation time of motion signals, suggesting that filters were products of sensory processing. By quantifying the effects of different levels of stimulus noise on pursuit, we have provided rigorous constraints for understanding sensory population decoding. We have shown how temporal and spatial integration of sensory signals converts noisy population responses into precise motor responses.

Beyond spatial averaging: Simulations of near-field scanning spectroscopy of quantum structures with interfacial roughness

2000 ◽  
Vol 62 (16) ◽  
pp. 11071-11075 ◽  
Author(s):  
Omar Di Stefano ◽  
Salvatore Savasta ◽  
Giovanna Martino ◽  
Raffaello Girlanda
Keyword(s):  
Near Field ◽  
Quantum Structures ◽  
Spatial Averaging ◽  
Interfacial Roughness ◽  
Scanning Spectroscopy ◽  
Near Field Scanning
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