scholarly journals Early, local motion signals generate directional preferences in depth ordering of transparent motion

2016 ◽  
Vol 16 (10) ◽  
pp. 24 ◽  
Author(s):  
Alexander C. Schütz ◽  
Pascal Mamassian
Keyword(s):  
Local Motion ◽  
Transparent Motion ◽  
Depth Ordering

scholarly journals Probabilities of perceptual depth ordering in transparent motion and the relative effect of different depth cues

2010 ◽  
Vol 7 (9) ◽  
pp. 744-744
Author(s):  
R. Moreno-Bote ◽  
A. Shipiro ◽  
J. Rinzel ◽  
N. Rubin
Keyword(s):  
Relative Effect ◽  
Depth Cues ◽  
Transparent Motion ◽  
Depth Ordering

Auroral event detection using spatiotemporal statistics of local motion vectors

2013 ◽  
Vol 24 (3) ◽  
pp. 175 ◽  
Author(s):  
Qian WANG ◽  
Jimin LIANG ◽  
Zejun HU
Keyword(s):  
Event Detection ◽  
Local Motion ◽  
Motion Vectors

Improving motion detection via anodal transcranial direct current stimulation

2020 ◽  
Vol 38 (5) ◽  
pp. 395-405
Author(s):  
Luca Battaglini ◽  
Federica Mena ◽  
Clara Casco
Keyword(s):  
Direct Current ◽  
Signal To Noise Ratio ◽  
Individual Performance ◽  
Coherent Motion ◽  
Motion Processing ◽  
Global Motion ◽  
Local Motion ◽  
Processing Efficiency ◽  
Temporal Area ◽  
Direct Current Stimulation

Background: To study motion perception, a stimulus consisting of a field of small, moving dots is often used. Generally, some of the dots coherently move in the same direction (signal) while the rest move randomly (noise). A percept of global coherent motion (CM) results when many different local motion signals are combined. CM computation is a complex process that requires the integrity of the middle-temporal area (MT/V5) and there is evidence that increasing the number of dots presented in the stimulus makes such computation more efficient. Objective: In this study, we explored whether anodal direct current stimulation (tDCS) over MT/V5 would increase individual performance in a CM task at a low signal-to-noise ratio (SNR, i.e. low percentage of coherent dots) and with a target consisting of a large number of moving dots (high dot numerosity, e.g. >250 dots) with respect to low dot numerosity (<60 dots), indicating that tDCS favour the integration of local motion signal into a single global percept (global motion). Method: Participants were asked to perform a CM detection task (two-interval forced-choice, 2IFC) while they received anodal, cathodal, or sham stimulation on three different days. Results: Our findings showed no effect of cathodal tDCS with respect to the sham condition. Instead, anodal tDCS improves performance, but mostly when dot numerosity is high (>400 dots) to promote efficient global motion processing. Conclusions: The present study suggests that tDCS may be used under appropriate stimulus conditions (low SNR and high dot numerosity) to boost the global motion processing efficiency, and may be useful to empower clinical protocols to treat visual deficits.

Local Motion Planning and Tracking of Autonomous Driving Vehicles*

2020 ◽  
Author(s):  
Wangwang Zhu ◽  
Xi Zhang ◽  
Baixuan Zhao ◽  
Shiwei Peng ◽  
Pengfei Guo ◽  
...  
Keyword(s):  
Motion Planning ◽  
Autonomous Driving ◽  
Local Motion

scholarly journals Deep Reinforcement Learning for End-to-End Local Motion Planning of Autonomous Aerial Robots in Unknown Outdoor Environments: Real-Time Flight Experiments

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2534
Author(s):  
Oualid Doukhi ◽  
Deok-Jin Lee
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Autonomous Navigation ◽  
Control Technique ◽  
Local Motion ◽  
Aerial Robots ◽  
Novel Approach ◽  
Outdoor Environments ◽  
Aerial Vehicle ◽  
High Level

Autonomous navigation and collision avoidance missions represent a significant challenge for robotics systems as they generally operate in dynamic environments that require a high level of autonomy and flexible decision-making capabilities. This challenge becomes more applicable in micro aerial vehicles (MAVs) due to their limited size and computational power. This paper presents a novel approach for enabling a micro aerial vehicle system equipped with a laser range finder to autonomously navigate among obstacles and achieve a user-specified goal location in a GPS-denied environment, without the need for mapping or path planning. The proposed system uses an actor–critic-based reinforcement learning technique to train the aerial robot in a Gazebo simulator to perform a point-goal navigation task by directly mapping the noisy MAV’s state and laser scan measurements to continuous motion control. The obtained policy can perform collision-free flight in the real world while being trained entirely on a 3D simulator. Intensive simulations and real-time experiments were conducted and compared with a nonlinear model predictive control technique to show the generalization capabilities to new unseen environments, and robustness against localization noise. The obtained results demonstrate our system’s effectiveness in flying safely and reaching the desired points by planning smooth forward linear velocity and heading rates.

V. Observations and experiments relating to the motion of the sap in vegetables

1714 ◽  
Vol 29 (349) ◽  
pp. 486-490 ◽  
Keyword(s):  
The Other ◽  
Local Motion ◽  
The One

Of Plants in general we may first observe, that they are either Terrestrial, Amphibious , or Aquatick ; and so nearly do Vegetables agree with Animals in most points, except Local Motion and its Consequences, that from the Knowledge of the one we are reasonably led to the Discovery of the other.

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