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Record W2113201845 · doi:10.1109/cvpr.1992.223193

Qualitative shape from active shading

2003· article· en· W2113201845 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaxima and minimaSurface (topology)Point (geometry)Aperture (computer memory)ShadingPhotometric stereoOpticsComputer scienceComputer visionGeometryPhysicsImage (mathematics)Artificial intelligenceMathematicsComputer graphics (images)Mathematical analysis

Abstract

fetched live from OpenAlex

It is shown how to actively compute qualitative shape properties of a surface directly from image intensities without first quantitatively reconstructing the surface. The approach diverges from classical active shape-from-shading in that two different types of lighting conditions (a diffuse source and a point source) are used, rather than two instances of a single type (a point source). There is no attempt to compute a dense depth map of the surface. Rather, the presence of certain qualitative geometric features is detected. In particular, a shape property, surface aperture, is introduced, and it is shown that under diffuse lighting there is typically a spatial correspondence between the minima of surface aperture and the minima of image intensity. Thus, the minima in surface aperture can be located. Different possible causes of the aperture minima can subsequently be distinguished by actively illuminating the surface with a point source positioned in the viewing direction.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.848
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.377
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it