scholarly journals Psychovisual Perception Scale Based on a Neural Network

2020 ◽  
pp. paper65-1-paper65-10
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Visual Perception ◽  
Brightness Distribution ◽  
Experimental Installation ◽  
Numerical Scale ◽  
Scale Category ◽  
Psychophysical Scale

The purpose of this article is to construct a psychophysical scale of visual perception from lighting scene based on a direct propagation neural network using for assessment of real or synthesized images with spatial brightness distribution. Visual perception assessments of different scenes were obtained for 10 observers at the experimental installation of the Department of lighting engineering of the MPEI (NRU). These results were checked and found out agreed with the numerical scale of visual perception proposed by Lekish and Holladay. Neural network was trained to predict a sensation at the level of 40-70%, depending on the scale category. For more careful prediction level in each of 5 categories of scale a new experiment should be done with new calibration and with tested instructions and with more observers involved. The novelty consists in using a neural network as an expert to assess the degree of comfort of the lighting scene.

Construction Of A Psychophysical Scale Of Visual Comfort of Lighting Based On A Neural Network: preparation Of The Experiment

2021 ◽  
pp. 114-122
Author(s):  
Vladimir P. Budak ◽  
Ekaterina I. Ilyina
Keyword(s):  
Neural Network ◽  
Research University ◽  
Confusion Matrix ◽  
Roc Curves ◽  
Arbitrary Distribution ◽  
Final Conclusion ◽  
Visual Comfort ◽  
Numerical Scale ◽  
Engineering Department ◽  
Psychophysical Scale

One of the important questions in lighting engineering is to determine the sensation of discomfort from lighting installations. There is no unified psychophysical scale for assessing the visual comfort of lighting (VCL) at any arbitrary distribution of luminance in space. This article considers a mathematical model of the scale based on a neural network (NN) as an ‘expert’ that trained to determine the comfort of perception of lighting depending on the light source’s luminance and background. The experimental data obtained at the lighting engineering department of the National Research University “MPEI” were used to train the NN. The experiment results presented in this article are consistent with the numerical scale for estimating the VCL proposed by Lakiesch and Holladay. A new model allows predicting the sensation of VCL with an accuracy of up to 70 %. This work allows formulating criteria for NN’s input and output parameters to choose a metric for evaluating NN’s performance, such as the confusion matrix, ROC curves, and a metric, such as the probability distribution for each sensation depending on the input parameters. It clearly follows, that amount of initial data is not allowed to make a final conclusion. One more experiment required considering the algorithm used to calibrate the experimental installation, instructions for observers, and the obtained results processing.

scholarly journals The rhythmic nature of visual perception

2018 ◽  
Vol 119 (4) ◽  
pp. 1251-1253 ◽  
Author(s):  
Randolph F. Helfrich
Keyword(s):  
Neural Network ◽  
Visual Perception ◽  
Brain Activity ◽  
Oculomotor System ◽  
Top Down ◽  
Discrete Sampling ◽  
The Neural Network ◽  
The World ◽  
Oscillatory Brain Activity

Our continuous perception of the world could be the result of discrete sampling, where individual snapshots are seamlessly fused into a coherent stream. It has been argued that endogenous oscillatory brain activity could provide the functional substrate of cortical rhythmic sampling. A new study demonstrates that cortical rhythmic sampling is tightly linked to the oculomotor system, thus providing a novel perspective on the neural network underlying top-down guided visual perception.

scholarly journals Driving Rhythm Method for Driving Comfort Analysis on Rural Highways

2017 ◽  
Vol 29 (4) ◽  
pp. 371-379 ◽  
Author(s):  
Bo Yu ◽  
Yuren Chen
Keyword(s):  
Neural Network ◽  
Visual Perception ◽  
Bp Neural Network ◽  
Shape Parameters ◽  
Rural Highways ◽  
Driving Speed ◽  
Variation Characteristics ◽  
Input Variables ◽  
New Perspective ◽  
Real Vehicle

Driving comfort is of great significance for rural highways, since the variation characteristics of driving speed are comparatively complex on rural highways. Earlier studies about driving comfort were usually based on the actual geometric road alignments and automobiles, without considering the driver’s visual perception. However, some scholars have shown that there is a discrepancy between actual and perceived geometric alignments, especially on rural highways. Moreover, few studies focus on rural highways. Therefore, in this paper the driver’s visual lane model was established based on the Catmull-Rom spline, in order to describe the driver’s visual perception of rural highways. The real vehicle experiment was conducted on 100 km rural highways in Tibet. The driving rhythm was presented to signify the information during the driving process. Shape parameters of the driver’s visual lane model were chosen as input variables to predict the driving rhythm by BP neural network. Wavelet transform was used to explore which part of the driving rhythm is related to the driving comfort. Then the probabilities of good, fair and bad driving comfort can be calculated by wavelets of the driving rhythm. This work not only provides a new perspective into driving comfort analysis and quantifies the driver’s visual perception, but also pays attention to the unique characteristics of rural highways.

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