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Record W2050424943 · doi:10.1142/s0219467812500040

CONTOUR INTERPOLATION USING LEVEL-SET ANALYSIS

2012· article· en· W2050424943 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

VenueInternational Journal of Image and Graphics · 2012
Typearticle
Languageen
FieldComputer Science
TopicMedical Image Segmentation Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInterpolation (computer graphics)Computer visionArtificial intelligenceCurvatureComputer scienceVisualizationTracingContour lineSet (abstract data type)Boundary (topology)TrajectoryMotion (physics)MathematicsGeometryGeographyCartography

Abstract

fetched live from OpenAlex

We propose a novel approach to generate intermediate contours given a sequence of object contours. The proposal unifies shape features through contour curvature analysis and motion between the contours through optic flow analysis. The major contribution of this work is in integrating this shape and image intensity-based contour interpolation scheme in a level-set framework. The interpolated contours between an initial and a target contour act as missing link and establish a path along which contour deformation has taken place. We have shown that for different application domains such as 3D organ visualization (the generation of contours between two spatially apart contours of 2D slice images of a 3D organ), the meteorological applications of tracing, and the path of a developing cyclone (when satellite images are taken at distant time points and the shape of cyclone in between two consecutive satellite images are of interest), the proposal has outperformed the competing approaches.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.703
Threshold uncertainty score0.253

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.054
GPT teacher head0.363
Teacher spread0.309 · 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