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Record W2131215230 · doi:10.1109/iccv.1990.139633

A new transform for curve detection

2002· article· en· W2131215230 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicImage and Object Detection Techniques
Canadian institutionsTechnical University of Nova Scotia
Fundersnot available
KeywordsHough transformCurve fittingParameter spacePoint (geometry)Enhanced Data Rates for GSM EvolutionComputationAlgorithmRange (aeronautics)Data pointComputer scienceSpace (punctuation)MathematicsArtificial intelligenceImage (mathematics)StatisticsGeometry

Abstract

fetched live from OpenAlex

A novel transform for curve detection, called the curve fitting Hough transform (CFHT), is proposed. In the conventional Hough transform (HT) and its variants, both storage and computation grow exponentially with the number of parameters. The CFHT is advantageous over the conventional HT and its variants in its high speed, small storage, arbitrary parameter range and high parameter resolution. This is achieved by fitting a segment of the curve to be detected to a small neighborhood of edge points. If the fitting error is less than a given tolerance, the parameters obtained from curve fitting are used to map an edge element to a single point in the parameter space. A multidimensional ordered parameter list is used to accumulate the presences of the curve to be detected. Most entries in the parameter list are 'useful' entries in the sense that they represent actual presences of the curves to be detected. Experimental results are presented.< <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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score0.207

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 head0.016
GPT teacher head0.232
Teacher spread0.216 · 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

Citations15
Published2002
Admission routes1
Has abstractyes

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