A neural network framework for low-level representation and processing in computer vision
Bibliographic record
Abstract
A goal of computer vision is the construction of scene descriptions based on information extracted from one or more 20 images. A reconstruction strategy based an a three-level representational framework is proposed. The first representational level, the primal sketch, makes explicit physical characteristics of the scene through detection of illuminance changes and their geometrical distribution and organization. Physical characteristics appear at several spatial scales and a multiresolution analysis helps in eliminating spurious edges. The second representational level, the raw 2.50 sketch, makes explicit the orientation and rough depth at edge location of the visible surfaces. A multiresolution neural network stereo algorithm is designed to compute the disparity at each edge location and at all the resolution levels. Matching is facilitated by a hierarchical focusing mechanism. The third representation level, the full 2.50 sketch, makes explicit the orientation and depth estimate at all the visible surface coordinates. Depth information between the edges is computed with a local shape-from-shading algorithm. A constraint satisfaction network fuses stereo and shading data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".