Fast Stereo Matching Algorithm for Intermediate View Reconstruction of Stereoscopic Television Images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a new stereo matching algorithm for intermediate view reconstruction of stereoscopic television images. The proposed stereo matching algorithm reduces the computational burden of disparity estimation by assuming that the disparity is constant over blocks of NtimesN pixels. The disparity per block is hierarchically estimated. A cost function deduced from maximum a posteriori disparity estimation is taken as a block similarity measurement for matching. To minimize the cost, an algorithm using a dynamic programming technique is proposed. The optimization algorithm considers the costs of N+2 possible nearest neighboring candidate block pairs, which have a maximum disparity difference of N pixels. Experimental results obtained with test image pairs show that a block size of 4times4 pixels was found to be the best for the image spatial resolution tested in this paper. Given this block size of 4times4 pixels, the computational burden of the proposed algorithm can be reduced by as much as 89%, compared to a reference algorithm that computes the disparity per pixel, without sacrificing picture quality
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it