Feature Point Based Polyp Tracking in Endoscopic Videos
Bibliographic record
Abstract
There has been various research conducted in polyp detection in endoscopic videos but not much has been done in the field of polyp tracking in endoscopic videos which itself is a very challenging task because of the nature of endoscopic videos. This paper discusses a modified method of polyp detection and proposes a feature point based polyp tracking method which is first in this field. Once polyp is detected, with Affine-SIFT (ASIFT) feature points extraction and matching we locate it in the next frame. Then we compute the polyp's image position with homography constraints and set up an interest window to accommodate it. In the tracking phase, we only focus on the interest window, detecting feature points from the window and updating the window's position and size until the polyps goes out of the frame. Results shows that Affine-SIFT (ASIFT) being a fully affine invariant image comparison method does works really well in endoscopic videos compares to SIFT and SURF.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".