A Possible Predictor of Visual Discomfort of Viewing Stereoscopic 3D Maps: The Imbalance of Disparity Distributions

2021 ◽  
Vol 65 (1) ◽  
pp. 1432-1436
Author(s):  
Ganyun Sun ◽  
William Liu ◽  
David Fraser ◽  
Yun Zhang
Keyword(s):  
Computational Model ◽  
Good Predictor ◽  
Stereo Images ◽  
Visual Discomfort ◽  
Stereoscopic 3D ◽  
Ieee Standard ◽  
Left And Right ◽  
3D Representation

Stereoscopic 3D (S3D) maps provide an accurate 3D representation of terrain texture for the precise perception of Earth’s surface. Visual discomfort on S3D images primarily comes from accommodation-vergence conflict, which is related to disparity (the distance between two corresponding points in the left and right stereo images). Previous studies have identified that disparity characteristics are related to visual discomfort. However, the relation between disparity characteristics and visual discomfort has not been investigated in orthographic S3D maps. It is unknown whether disparity characteristics are good indicators of visual discomfort regarding S3D maps. This study proposed a new visual discomfort predictor and compared it to the disparity characteristics already existing in the IEEE standard 3333.1.1™-2015. The comparisons indicate that the imbalance index can be a good predictor of visual discomfort regarding S3D maps. The predictor will be used in a personalized computational model to predict visual discomfort.

Structure from Stereo by Associative Learning of the Constraints

Perception ◽  
1989 ◽  
Vol 18 (6) ◽  
pp. 767-782 ◽  
Author(s):  
Alice J O'Toole
Keyword(s):  
Visual Field ◽  
Markov Process ◽  
Computational Model ◽  
Associative Learning ◽  
Input Data ◽  
Luminance Change ◽  
Depth Data ◽  
Graded Response ◽  
Left And Right ◽  
Spatial Locations

A computational model of structure from stereo that develops smoothness constraints naturally by associative learning of a large number of example mappings from disparity data to surface depth data is proposed. Banks of disparity-selective graded response units at all spatial locations in the visual field were the input data. These cells responded to matches of luminance change at convergent, divergent, or zero offsets in the left and right ‘retina’ samples. Surfaces were created by means of a pseudo-Markov process. From these surfaces, shaded marked and ummarked surfaces were created, along with random-dot versions of the same surfaces. Learning of these example shaded and shaded marked surfaces allowed the system to solve stereo mappings both for the surfaces it had learned and for surfaces it had not learned but which had been created by the same pseudo-Markov process. Further, the model was able to solve some random-dot versions of the surfaces when the surfaces had been learned as shaded marked surfaces.

A Risk-Aware Pairwise Rank Learning Approach for Visual Discomfort Prediction of Stereoscopic 3D

2019 ◽  
Vol 26 (11) ◽  
pp. 1588-1592 ◽  
Author(s):  
Qiuping Jiang ◽  
Feng Shao ◽  
Wei Gao ◽  
Hong Li ◽  
Yo-Sung Ho
Keyword(s):  
Learning Approach ◽  
Visual Discomfort ◽  
Stereoscopic 3D

Optimizational Model of Transit Line Chosen

2012 ◽  
Vol 178-181 ◽  
pp. 1887-1890
Author(s):  
Wen Bin Liu
Keyword(s):  
Graph Theory ◽  
Computational Model ◽  
Set Theory ◽  
Search Algorithm ◽  
Timing Function ◽  
Left And Right ◽  
Walking Time

In the paper,using graph theory,set theory and iteration,we give gradual search algorithm with number of transfer acting as parameter. Through dealing with data of traffic line, line is united in algorithm,and computational model is simplified. Through optimization of left and right in circuit site and same line repeating site, the shortest timing function in circuit is realized. We consider the subway and walking time of all sites furtherly.

scholarly journals PERANCANGAN VIDEO STEREOSCOPIC 3D DENGAN 2 KAMERA SMARTPHONE MENGGUNAKAN METODE COLOR ANAGLYPH

Respati ◽  
2017 ◽  
Vol 8 (23) ◽  
Author(s):  
Sucipto Sucipto
Keyword(s):  
Video Processing ◽  
3D Imaging ◽  
Average Score ◽  
Stereoscopic Video ◽  
Shooting Technique ◽  
Stereoscopic 3D ◽  
Average Value ◽  
A Value ◽  
Left And Right ◽  
The Brain

3D Stereoscopy also called stereoscopic or 3D imaging refers to a technique for creating or enhancing the illusion of depth to the picture by presenting two offset images separately to the left and right eye of the viewer. Two-dimensional images are then combined in the brain to give the perception of 3D depth. One method to represent the level of depth with stereoscopic. Two stereoscopic images are stacked, there will be the so-called image disparity (disparity image) that is separate duplicate images because of the different parallax. Disparity image is created because of stereobase, ie the distance between the camera lens axis. The distance between the two cameras, can be analogous to the distance of the two eyes are large 65 mm.                Factors that affect the stereoscopic video is video processing performed by optimization of standard anaglyph, with color correction and gamma level of the video, shooting technique and the distance between the object lens with the camera, it is supported by the change in value significantly between techniques establishing shot (ES), tracking shot (TS) and a panning shot (PS). tracking shot (TS). distance between an object lens with the camera also has an effect, on the first visible value of 75 lux at a distance object with camera lenses over 20 meters were taken using a technique establishing shot (ES) has a value score an average of 4.6 to 6.8 while tracking shot (TS) have the highest scores, with an average score of 6.6 to 9, while for objects recorded average value is the highest PK (Pusat Keramaian) object with an average score of 2.6 object followed by a PA (Pemandangan Alam) object with an average score of 2.4. Keyword : stereoscopic, disparity, parallax, stereobase, gamma.

Deep Visual Discomfort Predictor for Stereoscopic 3D Images

2018 ◽  
Vol 27 (11) ◽  
pp. 5420-5432 ◽  
Author(s):  
Heeseok Oh ◽  
Sewoong Ahn ◽  
Sanghoon Lee ◽  
Alan Conrad Bovik
Keyword(s):  
Visual Discomfort ◽  
3D Images ◽  
Stereoscopic 3D
Sign in / Sign up

Export Citation Format

Share Document