Pre-marking methods for 3D object recognition
Bibliographic record
Abstract
Two premarking methods are proposed for a new 3D object recognition system under development at the University of Toronto. In this system, an object is modeled using only a small number of 2D distinct perspective views (standard views) predefined wit the help of markers placed on the object. During the recognition process, a standard view is acquired by first determining its surface normal (standard-view axis), and then aligning the camera's optical axis with it. Standard-view axes are obtained by analyzing the images of the markers. A morphological skeleton transform (MST) is used for the extraction of required marker features. This work presents the analytical solution for the two proposed premarking schemes, based on circular markers, that can be used in acquiring standard views of objects. Specific issues addressed include: the determination of the perspective distortion and its relative importance, the determination of the transformation parameters required for camera alignment, and the use of a class of MST, pseudo-Euclidean skeletons, for feature extraction.>
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".