MétaCan
Menu
Back to cohort
Record W1483719133

A Triple-diagonal Gradient-based Edge Detection.

2003· article· en· W1483719133 on OpenAlexfundno aff
Qiang Wu, Xiangjian He, Tom Hintz

Bibliographic record

VenueUTS ePRESS (University of Technology Sydney) · 2003
Typearticle
Languageen
FieldComputer Science
TopicMedical Image Segmentation Techniques
Canadian institutionsnot available
FundersUniversity of California, IrvineNational Taiwan UniversityShanghai Jiao Tong UniversityZhejiang UniversityTechnische Universität BerlinUniversität WienHongik UniversityUniversity of BristolUniversidad EAFITUniversity of Massachusetts DartmouthChinese Academy of SciencesUniversity of ReginaUniversidad de MálagaCity University of Hong KongUniversidad Pública de NavarraNanyang Technological UniversityUniversity of MissouriUniversidad de NavarraUniversity of TorontoUniversity of BedfordshireDartmouth CollegeUniversity of Miami
KeywordsDiagonalEdge detectionEnhanced Data Rates for GSM EvolutionArtificial intelligenceMathematicsImage gradientMorphological gradientComputer visionComputer scienceGeometryAlgorithmPattern recognition (psychology)Image processingImage (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

Gradient-based edge detection is a straightforward method to identify the edge points in the original grey-level image. It is consistent with the intuition that in the human vision system the edge points always appear where the change of grey-level is greatest within their neighbourhood. In this paper, triple-diagonal gradient-based edge detection is introduced. It is based on the features of Spiral Architecture and computes the gradients in three diagonal directions instead of approximating the gradient in one direction only as the traditional methods do. Essentially, it improves the accuracy for locating edge points. As a result, it does not need any supplementary processing to enhance the edge map.

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.

How this classification was reachedexpand

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 score0.828
Threshold uncertainty score0.634

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueUTS ePRESS (University of Technology Sydney)Same topicMedical Image Segmentation TechniquesFrench-language works237,207