Bibliographic record
Abstract
Many photogrametric processes require a large number of points to be collected from numerous digital images. It is imperative that these points be collected accurately, so that precise real-world coordinates may be assigned to points captured in the image. To this end, many techniques have been developed to locate, track and identify image targets. This thesis outlines many of these techniques and presents a target matching solution that has been developed in C++, for the subpixel target location program INDMET . The target matching solution is composed of three elements: an epipolar line program, a cross correlation program and a template least squares matching program. The epipolar line program is used to limit the search area in the right image of a given stereo pair, to the vicinity of a single line. The cross correlation program searches this line to locate possible targets and the template least squares matching program is used to determine the target centre of a black and white image target, once it has been located. It was found that these three programs, working together, had between a 20 and 70 percent chance of locating the correct target, depending on the similarity of elliptical targets in each image. Once found, the program could calculate the target centre to an accuracy of approximately 1/10th of a pixel.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.018 |
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".