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Record W1976038972 · doi:10.4018/ijcicg.2014070102

Continuous Line Drawings and Designs

2014· article· en· W1976038972 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

VenueInternational Journal of Creative Interfaces and Computer Graphics · 2014
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceTracingBoundary (topology)Tree (set theory)Process (computing)Artificial intelligenceComputer visionKey (lock)Path (computing)Line (geometry)StereoscopyScratchBoundary lineFunction (biology)PixelTree traversalLine segmentTree structureAlgorithmGeometryMathematicsBinary tree

Abstract

fetched live from OpenAlex

Continuous Line Drawing (CLD) is a drawing style where a picture consists of a single closed non-intersecting line. This paper presents an automatic algorithm for constructing CLDs, with tone and structural information obtained from input images. The connectivity of the line is maintained through a tree generated by path finding with consideration of the key features for a given image. A branching tree structure is grown incrementally by selecting pixels by a cost function, relating to both the tone map and an importance map. After labeling each branch, an artificial wall is then constructed through a two-stage labeling propagation process to produce a single boundary, interpreted as the final CLD. The presented CLD method is effective and automatic, and provides some opportunities for variations. The paper also shows how to design CLDs from scratch using three steps: building base structures, forming shapes by thickening, and extracting CLDs by tracing the boundary of the shapes.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.858
Threshold uncertainty score0.411

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 science0.0010.000
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 head0.016
GPT teacher head0.288
Teacher spread0.272 · 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