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Record W2001106825 · doi:10.1115/detc2010-28903

Medial Axis Transform of Planar Shapes With Free-Form Curve Boundary

2010· article· en· W2001106825 on OpenAlexaff
Qiang Fu, Zezhong C. Chen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Numerical Analysis Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsBoundary (topology)Medial axisPlanarTracingTool pathProcess (computing)GeometryComputer scienceSimple (philosophy)Tree (set theory)AlgorithmMathematicsMathematical analysisComputer graphics (images)

Abstract

fetched live from OpenAlex

Medial axis transform (MAT) is a very useful shape interrogation tool in NC tool path generation for pocket milling. This paper presents a new, efficient approach to calculating MATs of planar profiles with boundaries of free-form curves. The proposed approach is mainly based on profile boundary tracing and decomposition. By studying the basic elements of MAT and their geometric properties, several algorithms of finding contact circles are developed. The boundary tracing algorithm can handle profiles with/without internal holes. For a profile without internal holes, it is continuously divided into simpler sub-profiles while travelling along the boundary, and a tree data structure is adopted to keep track of the boundary decomposition process. For a profile with internal holes, it is divided into several simple profiles without internal holes. After generating the MAT of each simple profile, the completed MAT can be found by combining these MATs. This proposed approach is implemented and some illustrative samples are presented to demonstrate its advantages.

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)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.610
Threshold uncertainty score0.464

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 communication0.0000.000
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 head0.004
GPT teacher head0.210
Teacher spread0.206 · 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 designBench or experimental
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

Citations2
Published2010
Admission routes1
Has abstractyes

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