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Record W120056940

Real-time video matting using multichannel poisson equations

2010· article· en· W120056940 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
FieldComputer Science
TopicImage Enhancement Techniques
Canadian institutionsUniversity of AlbertaMemorial University of Newfoundland
Fundersnot available
KeywordsComputer scienceArtificial intelligenceComputer visionPoisson distributionProcess (computing)Set (abstract data type)Image processingImage (mathematics)AlgorithmPoisson's equationPattern recognition (psychology)Mathematics
DOInot available

Abstract

fetched live from OpenAlex

A This paper presennts a novel matti ing algorithm for processing video sequences in reall-time and onlin ne. The algorithm is based on a set of novel Poissson equations that are derivved for handlinng multichannel coolor vectors, as well as the depth informatioon captured. A simmple yet effectiv ve approach is also proposed to compute an inittial alpha matte in the color space. Real-timme processing speed is achieved thro ough optimizing the algorithm for parallel processinng on the GPUs. To process live video sequences online and autonomously, a mod dified backgrounnd cut algorithm is immplemented to separate foreground and backgroound, the result of which guides the automatic trimap generation. Quantitative evaluation on stiill images show ws that the alphaa mattes extracted using the presented algorithm is much more accurate than the onnes obtained using the global Poisson matting algorithm and are comparable to that of other state-of-the-art offline image mattinng techniques.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.630
Threshold uncertainty score0.403

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 science0.0010.000
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 head0.021
GPT teacher head0.292
Teacher spread0.271 · 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

Citations14
Published2010
Admission routes1
Has abstractyes

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