Depth from Binocular Half-Occlusions in Stereoscopic Images of Natural Scenes
Bibliographic record
Abstract
Over the past two decades psychophysical experiments have firmly established that binocular half-occlusions are useful sources of information for the human visual system. The existing literature has focused on simplified stimuli that have no additional cues to depth, apart from stereopsis. From this large body of work we can be confident that the visual system is able to exploit binocular half-occlusions to aid depth perception; however, we do not know if this signal has any influence on perception when observers view complex stereoscopic stimuli with multiple sources of depth information. This issue is addressed here with the use of stereoscopic images of natural scenes, some of which have been digitally altered to manipulate a major half-occlusion signal. Our results show that depth-ordering judgments for these relatively complex stimuli are significantly affected by the nature of the half-occlusion signal, but only when highly textured surfaces are viewed. Under such conditions, the replacement of a binocular half-occlusion with background texture slows reaction time relative to performance when the occluded region is consistent with the foreground object. This result is specific to conditions when the depth ordering is correct (ie not reversed) and depends upon the size of the half-occlusion. The influence of the half-occlusion information in the presence of potent depth cues such as perspective, texture gradient, shading, and interposition is convincing evidence that this information plays a significant role in human depth perception.
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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".