Target design: a method for an accurate pose determination
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
New results related to a novel approach in computer vision are reported in this paper. Rather than designing ever more complex algorithms to extract given objects from images, the design of simple targets for given fast and robust algorithms is promoted. The mathematical nature of this approach leads to optimized target structures. A unifying model for vision algorithms is introduced which combines target primitive extraction and target structure matching. The usefulness of this approach is demonstrated by a practical example of a vision system. The system employs a line cluster as the target, with the Hough transform as the extraction algorithm. In order to get the best results, the performance of the vision system is evaluated. The evaluation is attempted on two bases, by a mathematical model and by experimental results. The mathematical model is capable of predicting the effects of various target parameters as well as the effects of various distortions of the vision system. Target design can lead to simpler, faster and more robust computer vision systems for use in industry.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it