Depth perception from motion parallax: dependence on texture spatial frequency and orientation
Bibliographic record
Abstract
Previous studies of motion parallax have employed random dot textures, which are broadband in terms of spatial frequency and orientation. However most neurons in the early visual system have specific tuning for spatial frequency and orientation. Furthermore, neurons selective for texture boundaries exhibit distinct tuning for high spatial frequency textures. Here we examine the effect of texture spatial frequency and orientation on depth perception from shear motion parallax. Visual stimuli consisted of textures created from randomly distributed Gabor micropatterns whose relative shearing motion was synchronized to the observer's horizontal head movements and modulated with a low spatial frequency (0.1 cpd), horizontal square wave envelope pattern. We measured psychophysical performance in a 2AFC depth-ordering task, for Gabor elements of varying spatial frequency (1 to 8 cpd) and orientation (vertical or horizontal). All of the Gabor micropatterns in each texture were of the same spatial frequency and orientation. Performance was measured for varying levels of added coherence noise, to obtain coherence noise thresholds. Furthermore, we varied the density and the contrast of Gabor micropatterns to measure the possible importance of sparseness and element contrast. At low spatial frequencies, performance was better for vertical than for horizontal Gabors while at high spatial frequencies (e.g. 8 cpd) there was no effect of orientation. However at mid-range spatial frequencies (e.g. 4 cpd), surprisingly, depth for most observers was better for horizontal than for vertical Gabors. Density of the micropatterns had little impact on psychophysical performance. Decrease in contrast increased the difference between performance for vertical and horizontal Gabor micropatterns. These results demonstrate that the mechanism for depth from motion parallax is highly dependent on the nature of the constituent surface textures. Meeting abstract presented at VSS 2014
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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 | 0.003 |
| 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".