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Record W2323994318 · doi:10.5594/m001406

Sensitivity to Monocular Occlusions in Stereoscopic Imagery: Implications for S3D Content Creation, Distribution and Exhibition

2010· article· en· W2323994318 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Imaging Technologies
Canadian institutionsYork University
Fundersnot available
KeywordsMonocularStereoscopyComputer visionArtificial intelligenceSensitivity (control systems)Computer scienceExhibitionComputer graphics (images)Content (measure theory)MathematicsArtVisual artsEngineering

Abstract

fetched live from OpenAlex

Since S3D requires two views of a scene, one for each eye, transformations such as reseating, 2D to S3D conversion, synthesis of multiview displays, coding and ADAT communications efficiency require generation of new views from 2D images. One of the main challenges to this process is the identification and treatment of monocularly occluded regions. In natural environments, monocular occlusions occur whenever objects are partially obstructed by other objects in a scene, giving rise to a region that is visible to only one eye. Experiments have shown that these regions influence depth percepts. Importantly, if monocular occlusion regions are presented with texture that is inconsistent with the surrounding regions, or with inappropriate geometry, depth is degraded. This paper will review the geometric basis of monocular occlusions and their role in natural depth perception. The analysis will be framed in the context of the reconstruction of novel and appropriate viewpoints from sequences of 2D images from one or more vantage points.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.704
Threshold uncertainty score0.330

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 head0.019
GPT teacher head0.273
Teacher spread0.254 · 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

Citations3
Published2010
Admission routes1
Has abstractyes

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