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Record W2124130976 · doi:10.1109/icassp.1988.196747

Morphological skeleton representation and shape recognition

2003· article· en· W2124130976 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDigital Image Processing Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTopological skeletonPattern recognition (psychology)Representation (politics)Skeleton (computer programming)Artificial intelligenceComputer scienceShape analysis (program analysis)Matching (statistics)Medial axisFeature extractionSimilarity (geometry)Feature (linguistics)Distance transformSet (abstract data type)Computer visionMathematicsImage (mathematics)Active shape modelSegmentation

Abstract

fetched live from OpenAlex

The nature of the morphological skeleton representation of a binary shape is related to the composition of structuring elements through the distance function defined by morphological set transforms in digital space. Two digital metrics, uniform-step distance and periodically-uniform-step distance, are introduced to provide useful spatial measures for morphological transforms. A natural shape representation by ribbonlike components is accomplished by extraction of skeletal feature primitives from the morphological skeleton of a shape. The hierarchical structure of the representation makes it stable and insensitive to noise disturbance. The matching is a simple top-down process in which the inverses of the skeletal feature primitives at each level are compared. The recognition is based on the similarity measure provided by the matching process.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score0.251

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 head0.051
GPT teacher head0.294
Teacher spread0.243 · 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

Citations31
Published2003
Admission routes1
Has abstractyes

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