Contribution of motion parallax to depth ordering, depth magnitude and segmentation
Bibliographic record
Abstract
Motion parallax, i.e. differential retinal image motion resulting from movement of the observer, provides an important visual cue to segmentation and depth perception. Previously we examined its role in segmentation (VSS 2009), and here we additionally explore its contribution to depth perception. Subjects performed lateral head translation while an electromagnetic tracker recorded head position. Stimuli consisted of random dots on a black background, whose horizontal displacements were synchronized proportionately to head motion by a scale factor (gain), and were modulated using square or sinewave envelopes to generate shearing motion. Subjects performed three tasks: depth ordering, depth magnitude and segmentation. In depth ordering they performed a 2AFC task, reporting whether the half-cycle above vs below the centre of the screen appeared nearer. Depth magnitude estimates were obtained by matching the perceived depth to that of a texture-mapped 3d surface of similar shape which was rendered in a perspective view. Segmentation performance was assessed by measuring discrimination thresholds for envelope orientation. This task included two conditions: one in which stimuli were synched to the head motion and the other in which previously recorded motions of the stimuli were “played-back”. For square wave modulation, good depth ordering performance was obtained only at low gain values; however sinewave modulation yielded unambiguous depth across a broader range of gains. In the depth magnitude task, subjects matched proportionately greater depths for larger gain values. In the segmentation task, orientation discrimination showed surprisingly similar thresholds for head motion and playback. These results suggest that the ecological range of depths in which motion parallax gives good segmentation is very wide, whereas for good depth perception it is quite limited. The dependence of depth ordering on modulation waveform suggests that motion parallax is more useful for depth differences within one object than between occluding objects.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".