Bibliographic record
Abstract
This thesis presents a reverse projection model for image formation, which is particularly useful for explaining blur. The model is used to develop novel methods for two different applications: removing the effects of blurred occluding objects and recovering the 3D structure of scenes from defocused images. With respect to depth recovery, the model shows that, when out of focus, multiple pixels record light reflected from the same region of the scene, giving rise to a measurable increase in the correlation between such pixels. Having found that increase to be proportional to scene depth, correlation measurements are used to estimate the depth of objects in a scene, giving a new method for the recovery of depth from defocus. In addition, this thesis presents a method by which this and other depth from defocus methods can be made more accurate by evolving the region over which blur is measured. Using an elliptical model for the measurement region, it is shown that a straightforward algorithm can be used to produce more accurate depth estimates near discontinuities in depth and surface orientation. With respect to occluding objects, the reverse projection blur framework is used to model image formation near large discontinuities in depth. This leads to a validated model that describes the way in which light from the foreground and background objects combine on a camera's sensor, and a method for the removal of the contribution of the occluding object. In order facilitate the removal of the occluding object's intensity from single images without user intervention, a method is developed to estimate the parameters necessary to remove this contribution.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".