Constrained Intensity-based Image Registration: Application to Aligning Human Back Images
Bibliographic record
Abstract
In this work, an accurate method to register multi-view images of the human torso is developed. In particular, a new framework that incorporates prior statistical knowledge about the registration is developed and tested. This framework leads to a computationally efficient procedure to accurately align images of the human torso. An intensity based image registration procedure is used to obtain the deformation fields by modelling them as both locally affine and globally smooth. Next, the estimated geometric deformation fields are analyzed in order to construct a prior deformation model. Two subspace analysis projection techniques are used to construct the subspaces of plausible deformations, namely principal component analysis (PCA) and independent component analysis (ICA). Accurate deformations are now guaranteed by projecting the locally computed geometric transformations onto the subspaces of plausible deformations. The proposed registration method was validated using high resolution images of the human torso. In order to handle the high resolution images, a multi-resolution framework was employed in the registration process. Experiments demonstrate promising performance in terms of mean square error and in the computational complexity. The main contribution of this work is the development of image registration method that uses subspace constraints to align images of the human torso. This method did not use the intra and inter image constraints used in most intensity based image registration algorithms in the literature.
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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 | 0.005 |
| 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) | 0.002 | 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".