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Record W2588309316 · doi:10.1109/etvis.2016.7851159

Gaze-contingent interactive visualization of high-dynamic-range imagery

2016· article· en· W2588309316 on OpenAlexafffund
Mahmoud Kalash, Karishma Singh, Rasit Eskicioglu, Neil D. B. Bruce

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Image and Video Retrieval Techniques
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Manitoba
KeywordsComputer scienceHigh dynamic rangeVisualizationDynamic rangeComputer visionRange (aeronautics)GazeComputer graphics (images)Artificial intelligenceTone mappingHigh-dynamic-range imagingGestureHuman–computer interactionEngineering

Abstract

fetched live from OpenAlex

There exist many types of visual imagery that span a dynamic range that exceeds what typical displays are able to capture. This implies that some information is lost, and requires schemes for compressing the dynamic range so that images are amenable to viewing on standard display technology. In certain application domains, specialized displays that cover a wider dynamic range are employed including for the display of medical images for diagnostic purposes. In this paper, we present a means of locally adapting the dynamic range of the display as a function of gaze location to allow for visualization of high dynamic range media on standard displays. This allows for viewing visual media over a dynamic range that exceeds limitations of any display, and presents additional value for certain application domains. The implemented system also allows for control over dynamic range globally based on hand gestures.

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.

How this classification was reachedexpand

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.903
Threshold uncertainty score0.249

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 communication0.0000.001
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 head0.297
Teacher spread0.287 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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".

Quick stats

Citations2
Published2016
Admission routes2
Has abstractyes

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