Bibliographic record
Abstract
Although our two eyes view the world from different perspectives, our brain can effortlessly associate items seen by one eye with those by the other - leading to the binocular depth sensation. We would like computers to make this match as well as humans, so that an intelligent system can perceive 3-D using binocular inputs from cameras. Current methods still have difficulty when two cameras are widely separated; more so when the images are of poor quality or contain repetitive patterns, because they can no longer distinguish features by examining only the local patches they occupy. Here we propose to improve the feature-matching by further involving global image information. We proposed a topological graph, called the Salient Feature Graph (SFG), to describe intrinsic structure of a scene based on its image. We then used the SFG to compare semi-local structures extracted from different perspectives. This semi-local comparison enabled our new algorithm, Context-Consistent Assignment (or CCA), to establish feature correspondences by dynamically involving both local appearance and global structures. We ran our algorithm and conventional methods on images of 3D urban scenes and counted how many correct matches they made. Our approach consistently outperformed competitors on difficult images such as low resolution inputs and noisy images.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".