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Record W1998985064 · doi:10.1145/1597990.1598058

Adaptive coded aperture projection

2009· article· en· W1998985064 on OpenAlex
Max Grosse, Gordon Wetzstein, Oliver Bimber, Anselm Grundhöfer

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
FieldEngineering
TopicAdvanced Optical Imaging Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsProjectorProjection (relational algebra)Computer visionComputer scienceAperture (computer memory)OpticsProjection planeArtificial intelligenceCoded apertureDepth of fieldAdaptive opticsPlane (geometry)Image planeCompensation (psychology)Computer graphics (images)PhysicsMathematicsImage (mathematics)AlgorithmAcousticsGeometry

Abstract

fetched live from OpenAlex

With adaptive coded aperture projection, we present solutions for taking projectors to the next level. By placing a programmable liquid crystal array at a projectors aperture plane we show how the depth of field (DOF) of a projection can be greatly enhanced. This allows focussed imagery to be shown on complex screens with varying distances to the projectors focal plane, such as projection domes as in planetariums or cylindrical canvases as in IMAX theaters. We demonstrate that adaptive apertures outperform previous methods of projector defocus compensation for objective lenses with static apertures. In addition, our adaptive apertures can perform the type of temporal contrast enhancement employed by common auto-iris projection lenses, and also produce high-quality depixelated images. The latter is beneficial for close-view displays with limited resolution, such as rear-projected TV sets.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.857
Threshold uncertainty score0.210

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 head0.010
GPT teacher head0.223
Teacher spread0.213 · 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

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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