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Record W1523232697 · doi:10.22456/2175-2745.5961

An Introduction to Light Interaction with Human Skin

2004· article· es· W1523232697 on OpenAlexafffund
Gladimir V. G. Baranoski, Aravind Krishnaswamy

Bibliographic record

VenueRevista de Informática Teórica e Aplicada · 2004
Typearticle
Languagees
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRendering (computer graphics)Computer scienceHuman skinComputer graphicsBiological tissueHuman healthLight scatteringHuman–computer interactionArtificial intelligenceScatteringComputer graphics (images)OpticsPhysicsMedicineBiology

Abstract

fetched live from OpenAlex

Despite the notable progress in physically-based rendering, there is still a long way to go before one can automatically generate predictable images of organic materials such as human skin. In this tutorial, the main physical and biological aspects involved in the processes of propagation and absorption of light by skin tissues are examined. These processes affect not only skin appearance, but also its health. For this reason, they have also been the object of study in biomedical research. The models of light interaction with human skin developed by the biomedical community are mainly aimed at the simulation of skin spectral properties which are used to determine the concentration and distribution of various substances. In computer graphics, the focus has been on the simulation of light scattering properties that affect skin appearance. Computer models used to simulate these spectral and scattering properties are described in this tutorial, and their strengths and limitations discussed. Keywords: natural phenomena, biologically and physically-based rendering.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0380.011

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 head0.007
GPT teacher head0.316
Teacher spread0.310 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations38
Published2004
Admission routes2
Has abstractyes

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