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Record W1754694348 · doi:10.1109/icpr.1988.28290

Reconstruction of two dimensional patterns by Fourier descriptors

2003· article· en· W1754694348 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 Retrieval and Classification Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsSignature (topology)Invariant (physics)Fourier transformFourier seriesComputer sciencePoint (geometry)AlgorithmAmplitudeMathematicsArtificial intelligencePattern recognition (psychology)GeometryMathematical analysisPhysicsOptics

Abstract

fetched live from OpenAlex

Two kinds of Fourier shape descriptors (FDs) are considered: ZR defined by C.T. Zahn and R.S. Roskies (1972) and G defined by G.H. Granlund (1972). In the first part of the paper ZR descriptors are studied. Two modifications of ZR descriptors are proposed. The new descriptors are based on step signature and smoother signature. The amplitudes of FDs are invariant under rotations, translations, changes in size, mirror reflections, and shifts in the starting point. In all the cases the reconstruction accuracy in terms of the number of FDs is studied, resulting in approximation error bounds. An efficient reconstruction method not requiring numerical integration is proposed for polygonal shapes. In the second part of the work theoretical results are verified in numerical experiments involving handwritten characters. In the same experiments, the performances of ZR and G descriptors are compared.< <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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.818
Threshold uncertainty score0.188

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 head0.242
Teacher spread0.226 · 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