Bibliographic record
Abstract
Most popular hand-crafted key-point detectors such as Harris corner, MSER, SIFT, SURF rely on some specific pre-designed structures for detection of corners, blobs, or junctions in an image. The very nature of pre-designed structures can be considered a source of inflexibility for these detectors in different contexts. Additionally, the performance of these detectors is also highly affected by non-uniform change in illumination. To the best of our knowledge, while there are some previous works addressing one of the two aforementioned problems, there currently lacks an efficient method to solve both simultaneously. In this paper, we propose a novel Sparse Coding based Key-point detector (SCK) which is fully invariant to affine intensity change and independent of any particular structure. The proposed detector locates a key-point in an image, based on a complexity measure calculated from the block surrounding its position. A strength measure is also proposed for comparing and selecting the detected key-points when the maximum number of key-points is limited. In this paper, the desirable characteristics of the proposed detector are theoretically confirmed. Experimental results on three public datasets also show that the proposed detector achieves significantly high performance in terms of repeatability and matching score.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".