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Record W2332432430 · doi:10.1115/msec2007-31196

Robust Machine Vision-Based Parts Inspection: Intelligent Neuro-Fuzzy Versus Threshold Based Classification

2007· article· en· W2332432430 on OpenAlex
Jonathan Killing, Brian Surgenor, Chris K. Mechefske

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
FieldEngineering
TopicIndustrial Vision Systems and Defect Detection
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMachine visionComputer scienceArtificial intelligenceFuzzy logicComputer visionNeuro-fuzzyRobustness (evolution)Fuzzy control systemMachine learningPattern recognition (psychology)

Abstract

fetched live from OpenAlex

Past experience with an industrial machine vision-based parts inspection system highlighted the need for a robust system, that is a vision system that could adapt to changes in the operating environment without requiring excessive retuning of the data analysis algorithm. With this need in mind, an intelligent neuro-fuzzy based image processing algorithm was developed and tested against a traditional threshold based algorithm. Experimental results indicate that the intelligent algorithm performs well when the data is not well segmented.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.921
Threshold uncertainty score0.830

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.068
GPT teacher head0.272
Teacher spread0.204 · 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