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Record W2381541746

A New Strategy of Edge Detection

2008· article· en· W2381541746 on OpenAlexvenueno aff
Haibo Hu

Bibliographic record

VenueMicrocomputer applications · 2008
Typearticle
Languageen
FieldComputer Science
TopicMedical Image Segmentation Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceEdge detectionBoundary (topology)Enhanced Data Rates for GSM EvolutionDimension (graph theory)Image (mathematics)Track (disk drive)AlgorithmTopology (electrical circuits)Computer visionArtificial intelligenceImage processingMathematical analysisMathematicsPure mathematics
DOInot available

Abstract

fetched live from OpenAlex

Selecting gradient threshold and detecting weak board are complex issues in edge detection of two-dimensional image. To overcome these two problems,this paper presents a new strategy of edge detection.Under the new strategy,two-dimension- al images are regarded as discrete samples of continuous functions separated by two-dimensional regular grids,and real boundary lines are the implicit continuous curves distributing in two-dimensional regular grids.Firstly,we detected and tracked the grids containing continuous boundary lines,and then,determined discrete edges according to the grids we detected.Because we can track the borders that have not been detected effectively by means of the connectivity of continuous boundary in our new strate- gy,we can overcome the above two complex issues effectively.

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: Methods
Teacher disagreement score0.894
Threshold uncertainty score0.365

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 science0.0010.000
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 head0.018
GPT teacher head0.264
Teacher spread0.246 · 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

Citations0
Published2008
Admission routes1
Has abstractyes

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