Depth and Perspective Perception of Flat Images in Static and Dynamic Visual Scenes
Bibliographic record
Abstract
The paper shows that a sense of depth can arise from two-dimensional (2D) scenes without the presence of a stereoscopic depth signal. Experimental information was obtained on three-dimensional (3D) visual perception of 2D static and dynamic scenes. The technique is based on fixing the conditions of eye movement during the perception of two-dimensional stimulus scenes. To obtain registration of the depth perception effects, they used volume and spatial perspective of 2D images (3D phenomenon), and a binocular eye tracker. The 3D phenomenon is identified using 3D raster images. It is assumed that the comparison of eye movements during a 3D raster image viewing allows you to identify uniquely the effects of the 3D phenomenon of stimulus planar scenes displayed on the monitor screen. The first part of the work shows the conditions for the emergence of a 3D phenomenon on two plots of dynamic and static scenes. The second part demonstrates the three-dimensional attributes of dynamic scenes with the highlighting of various video components. We emphasize that dynamic and static scenes are obtained directly from TV programs. The proposed graphical and mathematical method of analysis made it possible to show qualitatively the perception of the 3D phenomenon by KFU students and revealed the features of volume observation for planar images without the occurrence of binocular disparity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".