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Record W2094563755 · doi:10.1109/icassp.2010.5495348

Singular point detection using Discrete Hodge Helmholtz Decomposition in fingerprint images

2010· article· en· W2094563755 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
TopicBiometric Identification and Security
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFingerprint (computing)Singular point of a curveSingular value decompositionArtificial intelligenceMathematicsHelmholtz equationPoint (geometry)Fingerprint recognitionMatching (statistics)RidgePattern recognition (psychology)Singular spectrum analysisComputer visionComputer scienceAlgorithmMathematical analysisGeometryGeography

Abstract

fetched live from OpenAlex

Identification of singular points in the ridge structure is an important problem in fingerprint matching. This paper presents a fingerprint singular point detection method that is applicable to various fingerprint images regardless of their resolutions. Using the Discrete Hodge Helmholtz Decomposition (DHHD) method, potential structures of singular points can be extracted. We also calculate Poincare Index (PI) of the image. Then we combine DHHD and PI to detect singular points. The result of the experiment on the public databases with different images demonstrates that the proposed method has high accuracy in locating singular points.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.500
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.013
GPT teacher head0.287
Teacher spread0.274 · 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

Citations17
Published2010
Admission routes1
Has abstractyes

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