Boundary segmentation from dynamic occlusion-based motion parallax
Bibliographic record
Abstract
Active observer movement results in retinal image motion that is highly dependent on the scene layout. This retinal motion, often called motion parallax, can yield significant information about the boundaries between objects and their relative depth differences. Previously we examined segmentation from shear-based motion parallax, which consists of only relative motion information. Here, we examine segmentation from dynamic occlusion-based motion parallax, which contains both relative motion and accretion-deletion. We utilized random dots whose motion was modulated with vertical low spatial frequency envelopes and synchronized to head movements (Head Sync), or recreated using previously recorded head movement data for the same stationary observer (Playback). Observers judged the orientation of a boundary between regions of oppositely moving dots in a 2AFC task. The results demonstrate that observers perform poorer when the stimulus motion is synchronized to head movement, particularly at smaller relative depths, even though that head movement provides significant information about depth. Both expansion-compression and accretion-deletion in isolation could support segmentation, albeit with reduced performance. Therefore, unlike our previous results for depth ordering, expansion-compression and accretion-deletion contribute similarly to segmentation. Furthermore, human observers do not appear to utilize depth information to improve segmentation performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".