Space resection in photogrammetry using collinearity condition without linearisation
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
Space resection in photogrammetry involves determining the spatial position and orientation of a photo based on image measurements of ground control points that appear in the photo. Since space resection is a nonlinear problem, existing methods involve linearisation of the collinearity condition and the use of an iterative process to determine the final solution using the least-squares method. The process also requires initial approximate values of the unknown parameters, some of which must be estimated by another least-squares solution. This paper presents an optimisation model for space resection with or without redundancy that requires no linearisation, iterations, or initial approximate values. The model, which is nonlinear and noncovex, is solved using advanced Excel-based optimisation software that has been recently developed. Application of the model is illustrated using two numerical examples. The proposed model is simple and converges to the global optimal solution very quickly. The presented concept has many potential applications in solving nonlinear land surveying problems without the need for linearisation, and as such should be of interest to surveying and geomatics engineers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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