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Record W2109343068 · doi:10.1109/42.876306

A fast implementation of the minimum spanning tree method for phase unwrapping

2000· letter· en· W2109343068 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

VenueIEEE Transactions on Medical Imaging · 2000
Typeletter
Languageen
FieldComputer Science
TopicOptical measurement and interference techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMinimum spanning treePixelPhase (matter)Phase unwrappingComputer scienceSpanning treeImage processingComputer visionArtificial intelligenceAlgorithmImage (mathematics)OpticsMathematicsPhysicsInterferometryDiscrete mathematics

Abstract

fetched live from OpenAlex

A new implementation of the minimum spanning tree (MST) phase unwrapping method is presented. The time complexity of the MST method is reduced from O(n2) to O(n log2 n), where n is the number of pixels in the phase map. Typical 256 x 256 phase maps from magnetic resonance imaging can be unwrapped in seconds, compared with tens of minutes with the O(n2) implementation. This makes the pixel-level MST method time efficient and practically attractive. Index Terms-Image processing, magnetic resonance imaging, medical imaging, phase unwrapping.

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 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 score0.927
Threshold uncertainty score0.897

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.041
GPT teacher head0.367
Teacher spread0.326 · 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