MétaCan
Menu
Back to cohort
Record W2772584911 · doi:10.15353/vsnl.v3i1.179

Motion Detection in High Resolution Enhancement

2017· article· en· W2772584911 on OpenAlexafffundvenue
Xiaodan Hu, Avery Ma, Ahmed Gawish, Mark Lamm, Paul Fieguth

Bibliographic record

VenueJournal of Computational Vision and Imaging Systems · 2017
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsChristie (Canada)University of Waterloo
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsComputer visionArtificial intelligenceComputer scienceMotion estimationSuperposition principleMotion (physics)Quarter-pixel motionResolution (logic)Mathematics

Abstract

fetched live from OpenAlex

Shifted Superposition (SSPOS) is a resolution enhancement methodwhere apparent high-resolution content is displayed using a lowresolutionprojection system with an opto-mechanical shifter. WhileSSPOS-enhanced projectors have been showing promising resultsin still images, they still suffer from motion artifacts in video contents.Motivated by this, we present a novel approach to apparentprojector resolution enhancement for videos via motion-basedblurring module. We propose the use of a motion detection moduleand a blurring module to compensate for both SSPOS-resulted andnatural motion artifacts in the video content. To accomplish this,we combine both local and global motion estimation algorithms togenerate accurate dense flow fields. The detected motion regionsare enhanced using directional Gaussian filters. Preliminary resultsshow that the proposed method can produce accurate densemotion vectors and significantly reduce the artifacts in videos.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.299
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 source (direct Gemma or distilled Codex), 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

Citations1
Published2017
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Computational Vision and Imaging SystemsSame topicAdvanced Vision and ImagingFrench-language works237,207