Detecting scale-space consistent corners based on corner attributes
Bibliographic record
Abstract
Corner points are widely used as control points in computer vision, computer graphics, etc., because they are discrete and invariant to the scale and rotational change, which is an asset in many applications. Unfortunately, almost all the corner detectors failed to give consistent results over smoothing scale, which makes the use of the corners as control points unreliable for multi-resolution applications. To solve this, either adaptive smoothing or corner detection in multiple scales are applied. Both methods are very expensive computationally, and virtually impossible for real-time or time-constrained applications. From their study, the authors have reasons to believe that scale space persistent corners can be identified based on the the scale information. A new concept of significant value associated with a corner's detectability is introduced in this paper. A simple smoothing function is used to obtain analytical results. Such obtained results proved to be representative for general smoothing functions as well. The authors show that this value is strongly correlated with the scale-space behavior of corners and can be used to predict the corner behavior over the space scale. In other words, the authors are able to use this value to identify the scale space consistent corners based on the fine scale features only. The result is consistent with the studies of other literatures on this issue, and more important the scale space behavior of corners are quantified and measured by the corner attributes and neighboring features.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".