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Record W4238467796 · doi:10.1109/icpr.2004.1334509

Fast object localization using multi-scale image relevance function

2004· article· en· W4238467796 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

VenueProceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. · 2004
Typearticle
Languageen
FieldComputer Science
TopicVisual Attention and Saliency Detection
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsComputer visionArtificial intelligenceComputer scienceObject (grammar)Scale (ratio)Object detectionImage segmentationSegmentationImage textureOperator (biology)Function (biology)Image (mathematics)Relevance (law)Pattern recognition (psychology)Filter (signal processing)

Abstract

fetched live from OpenAlex

An object detection method using a model-based visual attention mechanism is proposed in application to visual inspection problems and medical diagnostic imaging. The proposed method is based on a multi-scale operator called image relevance function - a non-linear multi-scale filter bank that has local maxima at the centers of object locations. Model-based design of this operator takes into account intensity, shape and texture features of the objects to be detected. This approach offers several advantages, including fast and accurate object localization, simple extraction of shape features, adaptive segmentation of object regions.

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: Empirical · Consensus signal: none
Teacher disagreement score0.828
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.052
GPT teacher head0.294
Teacher spread0.242 · 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