Are blur and disparity complementary cues to depth?
Bibliographic record
Abstract
It has been claimed that disparity and blur are complementary cues to depth [Mather and Smith, 2000]. In particular, one study [Held et al 2012] has shown that depth discrimination from disparity is better near the fixation plane but depth discrimination from blur is better far beyond beyond the fixation plane. We carried out an experiment similar to Held et al, but we used shutter glasses for the stereo display rather than a volumetric display. Our stimuli consisted of pairs of dead leaves texture patterns which were visible through windows in the fixation plane. Viewing distance was 28 cm, rendered disparities were up to a few degrees, and presentation time was 250 ms. For each trial, subjects had to judge which of two texture patterns was farther in depth. Conditions included disparity+blur and disparity only (binocular) and blur only (monocular). The underlying assumption of the Held et al experiment is that increasing the disparity and/or blur causes a surface to be seen as farther away. We found, however, that this assumption failed for the majority of our subjects. The failure for disparity is not surprising since it has been shown that increasing disparity into the diplopic range can lead to a reduction in perceived depth [Richards and Kaye, 1974]. The failure for blur seems to be due to a tendency for subjects to perceive the more blurred stimulus as closer rather than further - despite the presence of the sharp window frame which is a cue that the blurred surface is beyond the window [Mather and Smith, 2002]. We conclude that if blur and disparity cues are combined to improve quantitative depth perception, then the rules of combination are more complicated than has been proposed up to now. 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".