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Silhouette Extraction in Hough Space

2006· article· en· W2123997651 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

VenueComputer Graphics Forum · 2006
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSilhouetteComputer scienceArtificial intelligencePolygon meshComputer visionOctreeComputer graphics (images)Tree traversalHough transformComputer graphicsProjection (relational algebra)Augmented realityImage (mathematics)Algorithm

Abstract

fetched live from OpenAlex

Abstract Object‐space silhouette extraction is an important problem in fields ranging from non‐photorealistic computer graphics to medical robotics. We present an efficient silhouette extractor for triangle meshes under perspective projection and make three contributions. First, we describe a novel application of 3D Hough transforms, which allows us to organize mesh data more effectively for silhouette computations than the traditional dual transform. Next, we introduce an incremental silhouette update algorithm which operates on an octree augmented with neighbour information and optimized for efficient low‐level traversal. Finally, we present a method for initial extraction of silhouette, using the same data structure, whose performance is linear in the size of the extracted silhouette. We demonstrate significant performance improvements given by our approach over the current state of the art . Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Three‐Dimensional Graphics and Realism]: Visible line/surface algorithms

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.781
Threshold uncertainty score0.653

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 head0.009
GPT teacher head0.256
Teacher spread0.247 · 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