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Record W2072183894 · doi:10.1117/1.1543158

Invariant object recognition under three-dimensional rotations and changes of scale

2003· article· en· W2072183894 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

VenueOptical Engineering · 2003
Typearticle
Languageen
FieldComputer Science
TopicImage and Object Detection Techniques
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInvariant (physics)Artificial intelligenceCognitive neuroscience of visual object recognitionCentroidComputer visionComputer science3D single-object recognitionScale spaceRotation (mathematics)Scale invariancePattern recognition (psychology)Classifier (UML)Feature extractionMathematicsImage processing

Abstract

fetched live from OpenAlex

Research on object recognition invariant under out-of-plane rotations has so far yielded limited results. The problem becomes even more complex when in addition scale changes must also be taken into account. We develop a new object recognition method invariant to translations, rotations, changes of pose, and scale. The method is based on angular wedge sampling about the centroid of the object, yielding translation-, rotation-, and scale-invariant features. A modified feature space trajectory classifier is used to obtain out-of-plane rotation invariance. The method is successfully tested on models of military land vehicles and is optically implementable.

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: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.686
Threshold uncertainty score0.243

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 head0.012
GPT teacher head0.207
Teacher spread0.195 · 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