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Record W1522158825 · doi:10.1109/iciap.1999.797625

Constructing iso-surfaces satisfying the Delaunay constraint. Application to the skeleton computation

2003· article· en· W1522158825 on OpenAlexaff
Dominique Attali, Jacques‐Olivier Lachaud

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicComputational Geometry and Mesh Generation
Canadian institutionsUniversity of Toronto
FundersEuropean Synchrotron Radiation Facility
KeywordsDelaunay triangulationConstrained Delaunay triangulationBowyer–Watson algorithmMathematicsVoronoi diagramTopological skeletonSurface (topology)Skeleton (computer programming)ComputationSurface triangulationMedial axisComputer scienceTopology (electrical circuits)CombinatoricsGeometryAlgorithmArtificial intelligence

Abstract

fetched live from OpenAlex

We design a new model for an image iso-surface which lies in the Delaunay graph of its vertices. Within each 8-cube of the image, a set of loops is computed according to the connectedness chosen for inner and outer voxels. Next, a triangulation is computed which respects the local geometry of these loops. Efficiency is obtained through the use of a look-up table which summarizes the algebraic tests that are required of each case. The inclusion of the iso-surface in the Delaunay triangulation has significant consequences. We derive a volume representation of the object, along with its skeleton. An example depicts the complete construction of our iso-surface, volume representation and skeleton computation.

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 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.863
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.014
GPT teacher head0.256
Teacher spread0.242 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations4
Published2003
Admission routes1
Has abstractyes

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