An Algorithm to Detect the Presence of 3D Target Motion from ISAR Data

2003 ◽  
pp. 223-240 ◽  
Author(s):  
Junfei Li ◽  
Hao Ling ◽  
Victor Chen
Keyword(s):  
Target Motion

scholarly journals Robust target motion analysis using the possibility particle filter

2019 ◽  
Vol 13 (1) ◽  
pp. 18-22 ◽  
Author(s):  
Branko Ristic ◽  
Jeremie Houssineau ◽  
Sanjeev Arulampalam
Keyword(s):  
Particle Filter ◽  
Motion Analysis ◽  
Target Motion ◽  
Target Motion Analysis

LS-SVM for a point target motion measurement

2014 ◽  
Author(s):  
Xin Li ◽  
Pengyu Guo ◽  
Hongliang Zhang ◽  
Xiaohu Zhang
Keyword(s):  
Target Motion ◽  
Motion Measurement ◽  
Point Target

Impact of Translational and Rotational Target Motion during Hypofractionated Prostate Radiotherapy

2009 ◽  
Vol 75 (3) ◽  
pp. S306-S307 ◽  
Author(s):  
J. Ye ◽  
F. Chen ◽  
M. Rao ◽  
R. Takamiya ◽  
D. Cao
Keyword(s):  
Target Motion ◽  
Prostate Radiotherapy

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.

Variability of Inter-fraction Target Motion During Conventional and Hypofractionated Lung Radiation Therapy

2014 ◽  
Vol 90 (1) ◽  
pp. S141-S142
Author(s):  
M.S. Jawad ◽  
S.M. Vance ◽  
I.S. Grills ◽  
H. Ye ◽  
S.K. Prausa ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Target Motion

scholarly journals Respiration correlated cone-beam computed tomography and 4DCT for evaluating target motion in Stereotactic Lung Radiation Therapy

2006 ◽  
Vol 45 (7) ◽  
pp. 915-922 ◽  
Author(s):  
Thomas G. Purdie ◽  
Douglas J. Moseley ◽  
Jean-Pierre Bissonnette ◽  
Michael B. Sharpe ◽  
Kevin Franks ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Radiation Therapy ◽  
Cone Beam Computed Tomography ◽  
Target Motion ◽  
Cone Beam

scholarly journals A Directionally Selective Small Target Motion Detecting Visual Neural Network in Cluttered Backgrounds

2020 ◽  
Vol 50 (4) ◽  
pp. 1541-1555 ◽  
Author(s):  
Hongxin Wang ◽  
Jigen Peng ◽  
Shigang Yue
Keyword(s):  
Neural Network ◽  
Target Motion ◽  
Small Target
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