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Record W2144010098 · doi:10.1109/icip.2009.5413426

Directional relationships to a reference object: A quantitative approach based on force fields

2009· article· en· W2144010098 on OpenAlexaff
Pascal Matsakis, Jingbo Ni, Melanie Veltman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicImage Retrieval and Classification Techniques
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPixelObject (grammar)Computer scienceComputationSet (abstract data type)Computer visionField (mathematics)Artificial intelligenceImage (mathematics)Simple (philosophy)Fuzzy logicAlgorithmMathematicsPure mathematics

Abstract

fetched live from OpenAlex

A directional map (or spatial template, fuzzy landscape) is an image where the value of each pixel represents the degree to which the pixel satisfies some directional relationship (e.g., right, left, above, below) to some reference object (i.e., a given set of pixels). There exists a simple quantitative model of such relationships, and the computation of directional maps is usually based on algorithmic implementations of this model. We show here that the model has important flaws, and we respond to the issue with a new, promising approach: all directional maps induced by the reference object are generated from a force field that the object (which is seen as a physical entity) creates around itself. Preliminary experiments illustrate and show the interest of the approach.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.913
Threshold uncertainty score0.305

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 communication0.0000.000
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 head0.081
GPT teacher head0.307
Teacher spread0.226 · 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 designTheoretical or conceptual
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

Citations3
Published2009
Admission routes1
Has abstractyes

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