Bibliographic record
Abstract
In the domain of 3D scene reconstruction this work presents the cooperation of shape from shading and stereo vision and demonstrates how to overcome a certain number of previously encountered problems. The problems of application assumptions, regularization terms and simplifications of physical models, used to overcome the problem of the modules of being ill-posed, are solved by the concept of integrating complementary knowledge of the physical world into one system. The problems due to the use of non-optimal resolution methods and too long parameter lists when the modules are integrated in a homogeneous system, are solved by the introduction of a cooperation concept for heterogeneous systems. The problem of error propagation from stereo vision to shape from shading, when only the initial and border conditions are used for the cooperation, is solved by the introduction of simultaneous constraints from both modules on all image points. The shape from shading problems of using too simple physical models for real scenes and inconsistent physical models with stereo vision are overcome by the introduction of more complex physical models. Perspective projection, point light sources and Phong's reflection model. The stereo vision problem caused by the lack of a global quality constraint when correlation is used as resolution method, is solved by using simulated annealing. The stereo vision problem arising from the use of the gray-levels for the resemblance constraint and so assuming lambertian surfaces, is solved by using the photometric characteristics from shape from shading instead.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".