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Record W1542003756 · doi:10.1109/ccece.1995.526406

Fuzzy detection of directions for the interpolation of images

2002· article· en· W1542003756 on OpenAlexaff
François Michaud, Chon Tam Le Dinh, G. Lachiver

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Compression Techniques
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsFilter (signal processing)Interpolation (computer graphics)Computer visionArtificial intelligenceFuzzy logicPixelComputer scienceComposite image filterMathematicsImage (mathematics)

Abstract

fetched live from OpenAlex

The performance of a vertical-temporal interpolation filter can be improved if detection of contour orientation in the spatial plane of the image is used to rotate the filter spatially. With this in view the authors have developed a direction detection filter based on the principles of fuzzy logic. This filter was designed for use with a linear-phase interpolation filter having three image fields. The fuzzy filter detects the direction of contours by considering the slight variations of pixels at five considered orientations, namely 90/spl deg/, /spl plusmn/45/spl deg/ and /spl plusmn/30/spl deg/. This article presents the characteristics of the developed fuzzy filter and its detection performance. The results of its use with a vertical-temporal interpolation filter are also presented, along with the methodology followed in definition of its membership functions.

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 score0.802
Threshold uncertainty score0.098

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 science0.0000.000
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 head0.024
GPT teacher head0.279
Teacher spread0.255 · 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
Published2002
Admission routes1
Has abstractyes

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