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Record W1672651452 · doi:10.1109/iscas.2002.1009958

Canny edge based image expansion

2003· article· en· W1672651452 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
TopicAdvanced Image Processing Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBilinear interpolationBicubic interpolationCanny edge detectorStairstep interpolationInterpolation (computer graphics)Image gradientArtificial intelligenceDeriche edge detectorComputer visionMathematicsEnhanced Data Rates for GSM EvolutionImage scalingPixelDemosaicingEdge detectionImage (mathematics)Computer scienceAlgorithmImage processingMultivariate interpolationBinary image

Abstract

fetched live from OpenAlex

In this paper, a Canny edge-based image expansion method is introduced. Our proposed expansion method outperforms the pixel replication, the bilinear interpolation and the bicubic interpolation methods. It gives crisp and less zigzag pictures. Our method is applied on the image after it has been expanded using bilinear or bicubic interpolation. The edges of such an expanded image are obtained using the Canny edge detector. The values of pixels around the edges are modified to yield a crisper and less zigzagged picture.

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: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.279
Threshold uncertainty score0.339

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 head0.012
GPT teacher head0.269
Teacher spread0.257 · 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

Citations56
Published2003
Admission routes1
Has abstractyes

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