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Optical Image Processing Using Light Modulation Displays

2010· article· en· W1970320385 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

VenueComputer Graphics Forum · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Imaging Technologies
Canadian institutionsUniversity of British Columbia
FundersNvidia
KeywordsComputer scienceComputer visionArtificial intelligenceMonocularFilter (signal processing)Contrast (vision)Image processingHuman visual system modelModulation (music)Computer graphics (images)Image (mathematics)

Abstract

fetched live from OpenAlex

Abstract We propose to enhance the capabilities of the human visual system by performing optical image processing directly on an observed scene. Unlike previous work which additively superimposes imagery on a scene, or completely replaces scene imagery with a manipulated version, we perform all manipulation through the use of a light modulation display to spatially filter incoming light. We demonstrate a number of perceptually motivated algorithms including contrast enhancement and reduction, object highlighting for preattentive emphasis, colour saturation, de‐saturation and de‐metamerization, as well as visual enhancement for the colour blind. A camera observing the scene guides the algorithms for on‐the‐fly processing, enabling dynamic application scenarios such as monocular scopes, eyeglasses and windshields.

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 score0.927
Threshold uncertainty score0.733

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 integrity0.0000.001
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 head0.009
GPT teacher head0.239
Teacher spread0.230 · 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