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Record W4235509516 · doi:10.1080/09500340210149651

Shift-, scale-, rotation- and pose-invariant object recognition using centroid wedge sampling and a feature space trajectory classifier

2003· article· en· W4235509516 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 Modern Optics · 2003
Typearticle
Languageen
FieldComputer Science
TopicImage and Object Detection Techniques
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsCentroidArtificial intelligencePattern recognition (psychology)Classifier (UML)Computer visionInvariant (physics)Cognitive neuroscience of visual object recognitionComputer scienceScale spaceScale invarianceFeature vectorFeature extraction3D single-object recognitionMathematicsImage processingImage (mathematics)Statistics

Abstract

fetched live from OpenAlex

Abstract We propose a new shift-, scale-, rotation- and pose-invariant method for three-dimensional object recognition. The method is based on angular scanning about the centroid of two-dimensional images of the objects yielding features invariant under changes in position, orientation and scale and a modified feature space trajectory classifier for pose invariance. Experimental results are shown and optical implementation is discussed.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.634
Threshold uncertainty score0.498

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 communication0.0000.001
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 head0.032
GPT teacher head0.261
Teacher spread0.229 · 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