ProCam Calibration using Single Pose of 2D Planar Target
Bibliographic record
Abstract
This thesis presents a simple and user friendly method for calibrating a procam system from a single pose of a 2D planar target. The camera intrinsic parameters, projector intrinsic parameters, procam system's extrinsic parameters and the distortion coe cients of the camera are returned using this method. Two sets of experiments were conducted: simulation experiments and real data experiments. The purpose of the simulation experiments were to test which poses of a camera and projector relative to a chessboard produce accurate calibration parameters. Several poses of a chessboard were imaged and used to calibrate a real procam system using the technique presented in this thesis and a Zhang-based method was used as the ground truth. The results of the real data experiments are congruent with the simulation experiments; accurate camera intrinsic parameters are returned when absolute sum of the X-axis and Y-axis rotation of the chessboard relative to the camera is greater than 20 degrees and accurate projector intrinsic parameters when the absolute value of the Y-axis rotation of the chessboard relative to the projector is greater than 10 degrees. When the conditions for valid camera and projector intrinsic parameters are met then accurate extrinsic parameters are returned as well.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".