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Record W1947284314 · doi:10.1109/ccece.1993.332409

Electrostatic field-based detection of corners of planar curves

2002· article· en· W1947284314 on OpenAlex
G.H. Abdel-Hamid, Yee-Hong Yang

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
FieldEngineering
TopicRobotics and Sensor-Based Localization
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCurvatureBoundary (topology)Point (geometry)EquipotentialField (mathematics)ElectrostaticsPlanarLine (geometry)Planarity testingAlgorithmComputer sciencePhysicsArtificial intelligenceGeometryMathematical analysisTopology (electrical circuits)MathematicsPure mathematicsComputer graphics (images)CombinatoricsQuantum mechanics

Abstract

fetched live from OpenAlex

A corner is often defined as the junction point between two or more straight line edges, or a point on the object's boundary curve having a curvature extremum. Our definition of a corner is a point having an electrostatic field extremum along an equipotential contour. Based on this definition, a novel approach for corner detection using electrostatic field theory is proposed. The approach is motivated by the simple observation that the electrostatic field is known to be concentrated around pointed conductor edges. It has the flavour of the multiscale-based approach of corner detection. In this paper, the relevant background on electrostatic field theory is reviewed. Corner detection using the proposed approach is then presented. Experimental results are then given to demonstrate the potential and merits of the electrostatic field-based approach.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.881
Threshold uncertainty score0.181

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 head0.009
GPT teacher head0.177
Teacher spread0.168 · 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

Citations5
Published2002
Admission routes1
Has abstractyes

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