A Random Sampling Model of Visual Information Acquisition

1992 ◽  
Author(s):  
Geoffrey Loftus ◽  
Thomas Busey ◽  
John Senders
Keyword(s):  
Information Acquisition ◽  
Random Sampling ◽  
Visual Information

Dynamic Resource Allocation for Compressive-Sensing-Based Wireless Visual Sensor Networks with Energy Harvesting

2013 ◽  
Vol 303-306 ◽  
pp. 187-190
Author(s):  
Lei You ◽  
Xin Su ◽  
Yu Tong Han
Keyword(s):  
Resource Allocation ◽  
Energy Harvesting ◽  
Compressive Sensing ◽  
Information Acquisition ◽  
Visual Information ◽  
Sensor Nodes ◽  
Visual Sensor ◽  
Visual Sensor Network ◽  
Available Bandwidth ◽  
Potential Applications

Wireless visual sensor network (WVSN) is emerging with many potential applications. The lifetime of a WVSN is seriously dependent on the energy shored in the battery of its sensor nodes as well as the adopted compression and resource allocation scheme. In this paper, we use the energy harvesting to provide almost perpetual operation of the networks and compressed-sensing-based encoding to decrease the power consumption of acquiring visual information at the front-end sensors. We propose a dynamic algorithm to jointly allocate power for both compressive-sensing-based visual information acquisition and data transmission, as well as the available bandwidth under energy harvesting and stability constraints. A virtual energy queue is introduced to control the resource allocation and the measurement rate in each time slot. The algorithm can guarantee the stability of the visual data queues in all sensors and achieve near-optimal performance.

scholarly journals Transfer Entropy Based Causality From Head Motion To Eye Movement

2021 ◽  
Author(s):  
Zezhong Lv ◽  
Qing Xu ◽  
Klaus Schoeffmann ◽  
Simon Parkinson
Keyword(s):  
Eye Movement ◽  
Information Acquisition ◽  
Visual Information ◽  
Transfer Entropy ◽  
Movement Behavior ◽  
Head Motion ◽  
Human Beings ◽  
Sensorimotor Activity ◽  
Set Up ◽  
Psychophysical Studies

AbstractEye movement behavior, which provides the visual information acquisition and processing, plays an important role in performing sensorimotor tasks, such as driving, by human beings in everyday life. In the procedure of performing sensorimotor tasks, eye movement is contributed through a specific coordination of head and eye in gaze changes, with head motions preceding eye movements. Notably we believe that this coordination in essence indicates a kind of causality. In this paper, we investigate transfer entropy to set up a quantity for measuring an unidirectional causality from head motion to eye movement. A normalized version of the proposed measure, demonstrated by virtual reality based psychophysical studies, behaves very well as a proxy of driving performance, suggesting that quantitative exploitation of coordination of head and eye may be an effective behaviometric of sensorimotor activity.

scholarly journals Providing a sensory basis for models of visual information acquisition

1993 ◽  
Vol 54 (4) ◽  
pp. 535-554 ◽  
Author(s):  
Geoffrey R. Loftus ◽  
Thomas A. Busey ◽  
John W. Senders
Keyword(s):  
Information Acquisition ◽  
Visual Information

scholarly journals Revealing Visual Information Use Through Random Sampling of Spatial Frequencies and Orientations

2021 ◽  
Vol 21 (9) ◽  
pp. 2801
Author(s):  
Francis Gingras ◽  
Jessica Limoges ◽  
Justin Duncan ◽  
Frédéric Gosselin ◽  
Daniel Fiset ◽  
...  
Keyword(s):  
Random Sampling ◽  
Visual Information ◽  
Information Use ◽  
Spatial Frequencies

scholarly journals Visual scanning strategies in the cockpit are modulated by pilots’ expertise: A flight simulator study

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0247061
Author(s):  
Christophe Lounis ◽  
Vsevolod Peysakhovich ◽  
Mickaël Causse
Keyword(s):  
Situation Awareness ◽  
Information Acquisition ◽  
Visual Information ◽  
Human Error ◽  
Flight Simulator ◽  
Transition Matrices ◽  
Visual Scanning ◽  
K Nearest Neighbors ◽  
Commercial Aviation ◽  
Scanning Strategies

During a flight, pilots must rigorously monitor their flight instruments since it is one of the critical activities that contribute to update their situation awareness. The monitoring is cognitively demanding, but is necessary for timely intervention in the event of a parameter deviation. Many studies have shown that a large part of commercial aviation accidents involved poor cockpit monitoring from the crew. Research in eye-tracking has developed numerous metrics to examine visual strategies in fields such as art viewing, sports, chess, reading, aviation, and space. In this article, we propose to use both basic and advanced eye metrics to study visual information acquisition, gaze dispersion, and gaze patterning among novices and pilots. The experiment involved a group of sixteen certified professional pilots and a group of sixteen novice during a manual landing task scenario performed in a flight simulator. The two groups landed three times with different levels of difficulty (manipulated via a double task paradigm). Compared to novices, professional pilots had a higher perceptual efficiency (more numerous and shorter dwells), a better distribution of attention, an ambient mode of visual attention, and more complex and elaborate visual scanning patterns. We classified pilot’s profiles (novices—experts) by machine learning based on Cosine KNN (K-Nearest Neighbors) using transition matrices. Several eye metrics were also sensitive to the landing difficulty. Our results can benefit the aviation domain by helping to assess the monitoring performance of the crews, improve initial and recurrent training and ultimately reduce incidents, and accidents due to human error.

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