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Record W2134404712 · doi:10.1109/icip.2007.4378916

A New Shape Signature for Fourier Descriptors

2007· article· en· W2134404712 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

VenueProceedings - International Conference on Image Processing · 2007
Typearticle
Languageen
FieldComputer Science
TopicImage Retrieval and Classification Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsZernike polynomialsFourier transformComputer scienceArtificial intelligenceImage retrievalCurvatureSignature (topology)Computer visionContent-based image retrievalImage (mathematics)Pattern recognition (psychology)MathematicsOpticsPhysicsGeometryMathematical analysis

Abstract

fetched live from OpenAlex

Shape-based image description is an important approach to content-based image retrieval (CBIR). A variety of techniques are reported in the literature that aim to represent objects based on their shapes; each of these techniques has its advantages and disadvantages. Fourier descriptor (FD), a simple yet powerful technique, has attractive properties such as rotational, scale, and translational invariance. In this paper we investigate this technique and present a novel shape registration method for extracting Fourier descriptors. When evaluated against curvature scale space (CSS) and Zernike moments (ZM) in shape-based image retrieval, the proposed technique exhibits superior performance.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
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 score0.674
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
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 head0.047
GPT teacher head0.322
Teacher spread0.276 · 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