Surrogate depth maps for stereoscopic imaging: different edge types
Bibliographic record
Abstract
Previously we demonstrated that surrogate depth maps, consisting of "depth" values mainly at object boundaries in the image of a scene, are effective for converting 2D images to stereoscopic 3D images using depth image based rendering. In this study we examined the use of surrogate depth maps whose depth edges were derived from cast shadows located in multiple images (Multiflash method). This method has the capability to delineate actual depth edges, in contrast to methods based on (Sobel) edge identification and (Standard Deviation) local luminance distribution. A group of 21 nonexpert viewers assessed the depth quality and visual comfort of stereoscopic images generated using these three methods on two sets of source images. Stereoscopic images based on the Multiflash method provided an enhanced depth quality that is better than the depth provided by a reference monoscopic image. Furthermore, the enhanced depth was comparable to that observed with the other two methods. However, all three methods generated images that were rated "mildly uncomfortable" or "uncomfortable" to view. It is concluded that there is no advantage in the use of the Multiflash method for creating surrogate depth maps. As well, even though the depth quality produced with surrogate depth maps is sufficiently good, the visual comfort of the stereoscopic images need to be improved before this approach of using surrogate depth maps can be deemed suitable for general use.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".