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Record W4312327775 · doi:10.1177/1071181322661300

Comparing two Designs for Removing edge Violation of Stereoscopic 3d Maps

2022· article· en· W4312327775 on OpenAlexaff
Ganyun Sun, Weilong Liu, Yun Zhang

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Imaging Technologies
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsStereoscopyEnhanced Data Rates for GSM EvolutionArtifact (error)Computer visionComputer scienceComputer graphics (images)Artificial intelligenceTerrainVisualizationGeography

Abstract

fetched live from OpenAlex

Stereoscopic 3D (S3D) maps present a faithful image of the Earth’s surface. A stereo edge violation occurs when an object appears to be in front of the screen and is cut off by the screen edge. Two designs are proposed to remove this artifact. One applies dynamic floating windows which cover the contents existing alone on the left or right edges of images. The other is to transform all the horizontal disparities to positive values; all the perceived 3D contents are behind the screen. This study compares the two designs with respect to user preference and visual fatigue. For the selected terrain scenes, participants preferred the design of pushing back to the design of dynamic floating windows. For visual fatigue, the design of dynamic floating windows obtained higher scores in Simplified Simulator Sickness Questionnaires (SSSQs) and General Eye Symptoms Questionnaires (GESQs), which indicated higher visual fatigue. This preliminary study observed that for removing edge violation of interactive S3D maps, the design of setting 3D contents behind the screen might be better than the design of dynamic floating windows in terms of user preference and visual fatigue.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score0.518

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 head0.032
GPT teacher head0.250
Teacher spread0.218 · 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
GenreEmpirical

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

Citations0
Published2022
Admission routes1
Has abstractyes

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