Maximum likelihood estimation of the distribution of target registration error
Bibliographic record
Abstract
Estimating the alignment accuracy is an important issue in rigid-body point-based registration algorithms. The registration accuracy depends on the level of the noise perturbing the registering data sets. The noise in the data sets arises from the fiducial (point) localization error (FLE) that may have an identical or inhomogeneous, isotropic or anisotropic distribution at each point in each data set. Target registration error (TRE) has been defined in the literature, as an error measure in terms of FLE, to compute the registration accuracy at a point (target) which is not used in the registration process. In this paper, we mathematically derive a general solution to approximate the distribution of TRE after registration of two data sets in the presence of FLE having any type of distribution. The Maximum Likelihood (ML) algorithm is proposed to estimate the registration parameters and their variances between two data sets. The variances are then used in a closed-form solution, previously presented by these authors, to derive the distribution of TRE at a target location. Based on numerical simulations, it is demonstrated that the derived distribution of TRE, in contrast to the existing methods in the literature, accurately follows the distribution generated by Monte Carlo simulation even when FLE has an inhomogeneous isotropic or anisotropic distribution.
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.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".