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Issue No.03 - May-June (2012 vol.32)
pp: 10-15
Gladimir V.G. Baranoski , University of Waterloo
Thomas Dimson , University of Waterloo
Tenn F. Chen , University of Waterloo
Bradley Kimmel , University of Waterloo
Daniel Yim , University of Waterloo
Erik Miranda , University of Waterloo
ABSTRACT
Light transport models are employed in applications in such varied areas as realistic image synthesis, noninvasive treatment of diseases, and remote sensing of natural resources. Openly accessible research resources can lead to significant advances involving these applications by fostering the cross-fertilization of different scientific disciplines. However, few light transport models have their source code openly available for download. Moreover, simply making the code available might not be enough; these models' complexity usually prevents their use beyond the research groups that developed them. The NPSGD (Natural Phenomena Simulation Group Distributed) framework makes light transport models easily accessible for online use. NPSGD acts a front end, connecting model implementations to the Web. It lets researchers perform predictive and time-intensive light transport simulations in a user-friendly, fault-tolerant way. More important, as a proof of concept, NPSGD demonstrates that the reproducibility of research results through model transparency is feasible. Such reproducibility can result in fruitful collaborations between model developers and users, regardless of their field of expertise.
INDEX TERMS
light transport, light transport models, spectral attributes, NPSGD, Natural Phenomena Simulation Group Distributed, computer graphics
CITATION
Gladimir V.G. Baranoski, Thomas Dimson, Tenn F. Chen, Bradley Kimmel, Daniel Yim, Erik Miranda, "Rapid Dissemination of Light Transport Models on the Web", IEEE Computer Graphics and Applications, vol.32, no. 3, pp. 10-15, May-June 2012, doi:10.1109/MCG.2012.58
REFERENCES
1. V. Tuchin, Tissue Optics: Light Scattering Methods and Instruments for Medical Diagnosis, 2nd ed., Int'l Soc. Optical Eng., 2007.
2. S. Jacquemoud and S. Ustin, “Modeling Leaf Optical Properties,” Photobiological Sciences Online, 2008; www.photobiology.infoJacq_Ustin.html.
3. J. Dorsey, H. Rushmeier, and F. Sillion, Digital Modeling of Material Appearance, Morgan Kaufmann, 2008.
4. W. Dorigo et al., “A Review of Reflective Remote Sensing and Data Assimilation Techniques for Enhanced Agroecosystem Modeling,” Int'l J. Applied Earth Observation and Geoinformation, vol. 9, no. 2, 2007, pp. 165–193.
5. T. Binzoni et al., “Detection Limits of Multi-spectral Optical Imaging under the Skin Surface,” Physics in Medicine and Biology, vol. 53, no. 3, 2008, pp. 617–636.
6. D. Yudovsky and L. Pilon, “Rapid and Accurate Estimation of Blood Saturation, Melanin Content, and Epidermis Thickness from Spectral Diffuse Reflectance,” Applied Optics, vol. 49, no. 10, 2010, pp. 1707–1719.
7. D. Yim et al., “A Cell-Based Light Interaction Model for Human Blood,” Computer Graphics Forum, vol. 31, no. 2, 2012, pp. 845–854.
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