MétaCan
Menu
Back to cohort
Record W2126131107 · doi:10.1117/1.2177638

Nonextensive information-theoretic measure for image edge detection

2006· article· en· W2126131107 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

VenueJournal of Electronic Imaging · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStatistical Mechanics and Entropy
Canadian institutionsConcordia University
Fundersnot available
KeywordsMeasure (data warehouse)Robustness (evolution)MajorizationDivergence (linguistics)Information theoryKullback–Leibler divergenceComputer scienceImage processingTsallis entropyEdge detectionImage (mathematics)Probability distributionMathematicsStatistical physicsArtificial intelligenceAlgorithmPattern recognition (psychology)StatisticsData miningDiscrete mathematicsPhysics

Abstract

fetched live from OpenAlex

We propose a nonextensive information-theoretic measure called Jensen-Tsallis divergence, which may be defined between any arbitrary number of probability distributions, and we analyze its main theoretical properties. Using the theory of majorization, we also derive its upper bounds performance. To gain further insight into the robustness and the application of the Jensen-Tsallis divergence measure in imaging, we provide some numerical experiments to show the power of this entopic measure in image edge detection.

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)0.000
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.327

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 head0.002
GPT teacher head0.209
Teacher spread0.207 · 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