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Record W2172145497 · doi:10.1109/icip.2002.1038990

Isophote estimation by cubic-spline interpolation

2003· article· en· W2172145497 on OpenAlexaff
Qing Wang, Rabab Ward, Hongjian Shi

Bibliographic record

VenueProceedings - International Conference on Image Processing · 2003
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Image Processing Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMonotone cubic interpolationSpline interpolationInterpolation (computer graphics)Bicubic interpolationPixelSpline (mechanical)Cubic Hermite splineMathematicsMultivariate interpolationNearest-neighbor interpolationThin plate splineSmoothing splineImage scalingComputer visionArtificial intelligenceComputer scienceImage (mathematics)PhysicsImage processingBilinear interpolation

Abstract

fetched live from OpenAlex

We apply the cubic-spline interpolation to estimate isophotes from sparsely sampled digital images. For any non-pixel, we interpolate it by cubic spline, and by solving the yielding cubic function analytically, we find positions of pixels with the same intensity value. Experiment results are given and discussed. This spreads some important light on the nature of interpolation in images and why the well-known zigzag effects are obtained when images are interpolated.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.318
Teacher spread0.293 · 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 designSimulation or modeling
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

Citations9
Published2003
Admission routes1
Has abstractyes

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