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Record W4232737280 · doi:10.1109/vg.2005.194102

Volume rendering for high dynamic range displays

2005· article· en· W4232737280 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueVolume graphics ... · 2005
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsUniversity of British Columbia
FundersMichael G. DeGroote Institute for Infectious Disease Research, McMaster University
KeywordsRendering (computer graphics)Volume renderingComputer scienceHigh dynamic rangeTone mappingComputer graphics (images)Real-time renderingDynamic rangeComputer vision3D renderingTransfer functionArtificial intelligence

Abstract

fetched live from OpenAlex

Dynamic range restrictions of conventional displays limit the amount of detail that can be represented in volume rendering applications. However, high dynamic range displays with contrast ratios larger than 50,000 : 1 have recently been developed. We explore how these increased capabilities can be exploited for common volume rendering algorithms such as direct volume rendering and maximum projection rendering. In particular, we discuss distribution of intensities across the range of the display contrast and a mapping of the transfer function to a perceptually linear space over the range of intensities that the display can produce. This allows us to reserve several just noticeable difference steps of intensities for spatial context apart from clearly depicting the main regions of interest. We also propose generating automatic transfer functions for order independent operators through histogram-equalization of data in perceptually linear space.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.928
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.015
GPT teacher head0.268
Teacher spread0.253 · 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