Assessment of depth magnitude from binocular disparity
Bibliographic record
Abstract
While binocular disparity is well known for providing high-resolution discrimination thresholds, it also plays an important role in defining the separation of features or objects in depth. This suprathreshold performance has been assessed using a variety of techniques, most of which involve visual and/or haptic transformations. Ideally, if these techniques accurately assess depth percepts, they would be interchangeable, and equally affected by factors such as experience. To test this prediction we compared the accuracy of three depth estimation methods (haptic sensor, digital caliper, and a virtual ruler) using a simple line stimulus, with groups of experienced and naïve observers. Participants were asked to estimate the amount of depth between two vertical bars using each estimation technique. We found no consistent difference between measurements regardless of the method used. However, while experienced observers’ estimates followed geometric predictions, naïve observers consistently under-estimated small, and over-estimated large disparities. One explanation for this difference is that naïve observers are more sensitive to the cue conflict between stereopsis and perspective foreshortening. To test this hypothesis, a second group of naïve observers were assessed using the original and a perspective-corrected configuration. Our results showed a significant effect of removing cue conflict at the largest test disparity only. Closer examination showed that the data were bi-modal. Removal of the conflict eliminated the estimation errors for half of the observers; the remaining observers were unaffected by the manipulation. We conclude that the three techniques evaluated are equally accurate for the configuration used here. A more important consideration is the amount of experience with such procedures. While some observers readily discount conflicting depth cues, others do not and these individuals may require additional training. Failure to take experience into account will result in high inter-observer variability and distorted depth estimates. Meeting abstract presented at VSS 2015
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".