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Record W4381280127 · doi:10.1115/1.4062634

A Novel Approach to Line Clipping Against a Rectangular Window

2023· article· en· W4381280127 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 Computing and Information Science in Engineering · 2023
Typearticle
Languageen
FieldComputer Science
TopicComputational Geometry and Mesh Generation
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsClipping (morphology)Robustness (evolution)Computer scienceComputer graphicsGraphicsWindow (computing)AlgorithmComputer graphics (images)Artificial intelligence

Abstract

fetched live from OpenAlex

Abstract Line clipping against a rectangular window is a fundamental problem in computer graphics. A robust and fast algorithm is needed not only for the traditional graphics pipeline but also for new applications, including web maps, nanomaterials, and sensor measurements. In this paper, we present a novel approach, which is based on the idea of combining the geometric and algebraic approaches. In particular, the proposed approach first decomposes a 2D line clipping problem into a set of 1D clipping problems, and then solves the 1D clipping problem by the comparison (i.e., >, <, and =) operation on the coordinate value of the projected points on one dimension only. Both theoretical analysis and experimental tests were conducted to demonstrate the improved robustness (for degenerated cases) and computational efficiency of the proposed approach.

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)0.002
metaresearch head score (Gemma)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.482
Threshold uncertainty score0.279

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0000.004
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 head0.016
GPT teacher head0.251
Teacher spread0.236 · 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