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Record W1945229394 · doi:10.1109/iscas.2004.1329421

Sobel edge detection processor for a real-time volume rendering system

2004· article· en· W1945229394 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
TopicComputer Graphics and Visualization Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSobel operatorComputer scienceRendering (computer graphics)Computer graphics (images)Real-time renderingVolume renderingVolume (thermodynamics)Edge detectionComputer visionImage processingImage (mathematics)

Abstract

fetched live from OpenAlex

This paper describes a novel fast and low-power Sobel edge detection processor targeted for image processing and volume rendering applications. The Sobel processor was built as a part of the real-time shear-warp factorization volume rendering system to compute a gradient. Sobel operator processor was designed and implemented in 0.18 /spl mu/m CMOS technology. Optimizations made at the mathematical model led to a simple regular architecture. High speed and low power consumption were achieved due to implementation of pipelining and parallelism at the components level. Employing the non-full swing CPL to design the Sobel processor sub-components reduced the power-delay product up to 40%. Simulation results showed that processor achieved the worst-case delay time of 4.61 ns and dissipates an average of 8.24 mW at 1.8 V and 200 MHz.

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 score0.955
Threshold uncertainty score0.423

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 communication0.0000.000
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 head0.015
GPT teacher head0.263
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