P‐2.3: Research on the Threshold of Brightness Difference of “Moving Head Pattern”

2021 ◽  
Vol 52 (S2) ◽  
pp. 714-715
Author(s):  
Shijun Wang ◽  
Tengfei Ding ◽  
Zhai Wei ◽  
Bo Feng ◽  
Yuke Tai ◽  
...  
Keyword(s):  
Brightness Difference

Brightness Difference and Its Relation to the Distinctness of a Border

1971 ◽  
Vol 61 (7) ◽  
pp. 962 ◽  
Author(s):  
PETER K. KAISER ◽  
THOMAS S. GREENSPON
Keyword(s):  
Brightness Difference

Study on the Relationship between the Traffic Characteristics and Speed at the Access of Urban Tunnels

2015 ◽  
Vol 743 ◽  
pp. 568-574
Author(s):  
Xiao Dan Xu ◽  
Li Lin
Keyword(s):  
Factor Analysis ◽  
Prediction Model ◽  
Operating Speed ◽  
Analysis Method ◽  
Time Headway ◽  
Traffic Characteristics ◽  
Factors Influencing ◽  
Brightness Difference ◽  
The Relationship ◽  
Mean Time

The present work sought to extract and quantify the factors influencing the speed at the access of the urban tunnels by factor analysis method and then found out that the principle factors influencing operating speed were cart ratio, mean time headway, brightness difference between inside and outside of the cave. Finally through the SPSS statistics software and on the base of analysis of multi-line regression, the prediction model of operating speed was established.

A brightness difference threshold meter for the evaluation of glare from light sources

1935 ◽  
Vol 12 (6) ◽  
pp. 177-185 ◽  
Author(s):  
B H Crawford ◽  
W S Stiles
Keyword(s):  
Difference Threshold ◽  
Light Sources ◽  
Brightness Difference

Model-Based GA Evolutionary 3-D Position Measurement Method

2012 ◽  
Vol 226-228 ◽  
pp. 1866-1871
Author(s):  
Xu Liu ◽  
Wei Song ◽  
Ya Nan Zhang ◽  
Xiao Lei Li
Keyword(s):  
Fitness Function ◽  
Simultaneous Detection ◽  
Internal Surface ◽  
Target Object ◽  
Grayscale Image ◽  
Position Measurement ◽  
Recognition Method ◽  
Model Based ◽  
Position Detection ◽  
Brightness Difference

This paper presents a vision-related technique for 3-D position measurement. The proposed method utilizes the genetic algorithm (GA) and unprocessed grayscale image input from vision, in order to perform recognition of a target being imaged with known target object shape. The problem to recognize the target shape and simultaneous detection of the position, is converted to an optimistic problem of a model-based evaluation function, named as surface-strips model-based fitness function that consists in the computation of the brightness difference between an internal surface and a contour-strips. In order to evaluate the proposed 3-D recognition method, experiments by an unprocessed grayscale image have been input to recognize a ball in the image. The results show the effectiveness of this method for 3-D position detection.

Effects of Brightness, Hue, and Saturation on Perceived Depth between Adjacent Regions in the Visual Field

Perception ◽  
1983 ◽  
Vol 12 (2) ◽  
pp. 167-175 ◽  
Author(s):  
Hiroyuki Egusa
Keyword(s):  
Visual Field ◽  
Brightness Difference ◽  
The Subject ◽  
Saturation Difference

The effects of brightness, hue, and saturation on perceived depth between adjacent regions have been examined. The stimulus consisted of two hemifields of different colors, and the subject was asked to state which appeared nearer and to judge the perceived depth between them. When both hemifields were achromatic, the perceived depth was found to increase with increasing brightness difference. Some subjects tended to judge the brighter side nearer, others the darker side nearer. With the achromatic–chromatic combination, there were no differences in perceived depth among three hue conditions, whilst with the chromatic–chromatic combination the perceived depth depended on hue combination. In terms of decreasing frequency of ‘nearer’ judgments the hue order was red, green, blue. When the two hemifields differed only in saturation, the perceived depth increased with increasing saturation difference, and whether the more saturated or the less saturated side was judged nearer depended on hue. It is argued that the effects of brightness and saturation on perceived distance from the observer can be attributed to figure–ground differentiation between adjacent regions in the visual field; but this argument does not cover the effect of hue under achromatic background conditions.

scholarly journals Fast pupil tracking based on the study of a boundary-stepped image model and multidimensional optimization Hook-Jives method

2021 ◽  
Vol 20 (2) ◽  
pp. 435-462
Author(s):  
Yuriy Grushko ◽  
Roman Parovik
Keyword(s):  
Real Time ◽  
Three Dimensional ◽  
Detection Accuracy ◽  
Fast Method ◽  
Image Model ◽  
Dimensional Parameter ◽  
Step Model ◽  
Brightness Difference ◽  
And Mathematics ◽  
Pupil Tracking

A new fast method for pupil detection and eyetracking real time is being developed based on the study of a boundary-step model of a grayscale image by the Laplacian-Gaussian operator and finding a new proposed descriptor of accumulated differences (point identifier), which displays a measure of the equidistance of each point from the boundaries of some relative monotonous area (for example, the pupil of the eye). The operation of this descriptor is based on the assumption that the pupil in the frame is the most rounded monotonic region with a high brightness difference at the border, the pixels of the region should have an intensity less than a predetermined threshold (but the pupil may not be the darkest region in the image). Taking into account all of the above characteristics of the pupil, the descriptor allows achieving high detection accuracy of its center and size, in contrast to methods based on threshold image segmentation, based on the assumption of the pupil as the darkest area, morphological methods (recursive morphological erosion), correlation or methods that investigate only the boundary image model (Hough transform and its variations with two-dimensional and three-dimensional parameter spaces, the Starburst algorithm, Swirski, RANSAC, ElSe). The possibility of representing the pupil tracking problem as a multidimensional unconstrained optimization problem and its solution by the Hook-Jeeves non-gradient method, where the function expressing the descriptor is used as the objective function, is investigated. In this case, there is no need to calculate the descriptor for each point of the image (compiling a special accumulator function), which significantly speeds up the work of the method. The proposed descriptor and method were analyzed, and a software package was developed in Python 3 (visualization) and C ++ (tracking kernel) in the laboratory of the Physics and Mathematics Faculty of Kamchatka State University of Vitus Bering, which allows illustrating the work of the method and tracking the pupil in real time.

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