Evaluation of constructable match cost measures for stereo correspondence using cluster ranking
Bibliographic record
Abstract
Stereo correspondence research often involves the comparison of techniques to determine which are better under different circumstances. The methods of comparison employed often take the form of applying the techniques to a few stereo image pairs with the technique with the lowest error rate declared superior. However, the majority of these comparisons do not contain any discussion of statistical significance; making the declared superiority of a technique statistically unreliable. In this paper we present a new evaluation method called cluster ranking that yields a statistically significant comparison of the stereo techniques being compared. Cluster ranking leverages statistical inference techniques to first rank the performance of stereo techniques on a single stereo image pair and then combine the rankings from multiple stereo pairs into an over-all ranking; in both of these rankings, only stereo techniques that are statistically different are given different ranks. We demonstrate our framework with a comparison of constructable match cost measures (those that can be assembled from a base set of components) on a data set consisting of 30 synthetic stereo pairs, with varying amounts of noise, and 18 scenes from the 2005 and 2006 Middlebury data sets. Our analysis reveals match cost measures, and measure components, that are statistically superior to all other measures depending on amount of noise, illumination, or exposure time.
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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 | 0.015 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".