The lightspace change constraint equation (LCCE) with practical application to estimation of the projectivity+gain transformation between multiple pictures of the same subject matter
Bibliographic record
Abstract
For many years, the brightness constancy constraint equation (BCCE) has been used for optical flow and related computer vision computations. However, almost all cameras have some kind of automatic exposure feature such as automatic gain control (AGC), so that the overall exposure level of the image varies as the camera is aimed at brighter or darker portions of a scene. Moreover, because most cameras have some kind of unknown nonlinear response function, the change due to AGC cannot be captured by merely applying a multiplicative constant to the pixels of each image. We propose, therefore, a lightspace change constraint equation (LCCE) that accounts for exposure change (AGC) together with the nonlinear response function of the camera. The response function can be automatically "learned" by an intelligent image processing system presented with differently exposed captures of the same subject matter in overlapping regions of registered images. Most importantly, a logarithmic lightspace change constraint equation (LLCCE) is shown to have a very simple mathematical formulation. The LCCE (and log LCCE) is applied to the estimation of the projective coordinate transformation between pairs of images in a sequence, and is compared with examples where the BCCE fails.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".