Hybrid 3D Dynamic Measurement by Particle Swarm Optimization and Photogrammetric Tracking
Bibliographic record
Abstract
Abstract High-accuracy motion modeling in three dimensions via digital images has been increasingly the matter of interest in photogrammetry and computer vision communities. Although accurate sub-pixel image registration techniques are the key elements of measurement, they still demand enhanced intelligence, autonomy, and robustness. In this paper, a new correlationbased technique of stereovision is proposed to perform inter-frame feature tracking, inter-camera image registration, and to measure the 3D state vector of features simultaneously. The developed algorithm is founded on population-based intelligence (particle swarm optimization) and photogrammetric modeling. The proposed technique is mainly aimed at reducing the computational complexities of non-linear optimization methods of digital image registration for deformation measurement, and passing through 2D image correlation to 3D motion modeling. The preliminary results have illustrated the feasibility of this technique to detect and measure sub-millimeter deformations by performing accurate, sub-pixel image registration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".