Adaptive reconstruction of intermediate views from stereoscopic images
Bibliographic record
Abstract
This paper deals with disparity estimation and the reconstruction of intermediate views from stereoscopic images. Using block-wise maximum-likelihood (ML) disparity estimation, it was found that the Laplacian model outperformed the Cauchy and Gaussian models in terms of disparity compensation errors and the number of correspondence matches. The disparity values in occluded regions were then determined using both object-based and reliability-based interpolation. Finally, an adaptive technique was used to interpolate the intermediate views. One distinguishing characteristic of this algorithm is that the left and right-eye images were projected onto the plane of the intermediate view to be reconstructed. This resulted in two projected images. The intermediate view was created using a weighted average of these two projected images with the weights based on the quality of the corresponding areas of the projected images. Subjective examination of the reconstructed images indicate that they have high image quality and good stable depth when viewed stereoscopically. An objective evaluation with the test image sequence "Flower Garden" shows that the proposed algorithm can achieve a peak signal-to-noise ratio gain of around 1 dB, when compared to a reference algorithm.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".