MétaCan
Menu
Back to cohort
Record W2679995671 · doi:10.1109/ccece.2017.7946595

Spatially constrained non-Gaussian mixture model for image segmentation

2017· article· en· W2679995671 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicBayesian Methods and Mixture Models
Canadian institutionsConcordia University
FundersMitacsConcordia University
KeywordsImage segmentationMixture modelArtificial intelligencePattern recognition (psychology)Markov random fieldExpectation–maximization algorithmComputer scienceScale-space segmentationPixelDirichlet distributionSegmentation-based object categorizationSegmentationComputer visionMathematicsMaximum likelihoodStatistics

Abstract

fetched live from OpenAlex

A new color image segmentation algorithm based on spatial information with the Dirichlet mixture model is presented. This method uses Markov Random Field to incorporate spatial information between neighboring pixels into a Dirichlet mixture model. The segmentation model is learned using Expectation Maximization (EM) algorithm based on Newton Raphson step. The obtained results using real image data set are more encouraging than those obtained using similar approaches.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.888
Threshold uncertainty score0.526

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 communication0.0010.001
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 head0.024
GPT teacher head0.313
Teacher spread0.289 · 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

Quick stats

Citations3
Published2017
Admission routes2
Has abstractyes

Explore more

Same topicBayesian Methods and Mixture ModelsFrench-language works237,207