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Proceedings Paper

Computational hair quality categorization in lower magnifications
Author(s): Barmak Heshmat; Hayato Ikoma; Ik Hyun Lee; Krishna Rastogi; Ramesh Raskar
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Paper Abstract

We take advantage of human hair specific geometry to visualize sparse submicron cuticle peelings with highly oblique tip-side illumination. We show that the statistics of these features can directly estimate hair quality in much lower magnifications (down to 20x) with less powerful objectives when the features themselves are below the system resolution. Our technique has strong potential for lower cost, portable, and autonomous hair diagnostic apparatuses.

Paper Details

Date Published: 10 March 2015
PDF: 6 pages
Proc. SPIE 9333, Biomedical Applications of Light Scattering IX, 93330Z (10 March 2015); doi: 10.1117/12.2078027
Show Author Affiliations
Barmak Heshmat, MIT Media Lab. (United States)
Hayato Ikoma, MIT Media Lab. (United States)
Ik Hyun Lee, MIT Media Lab. (United States)
Krishna Rastogi, MIT Media Lab. (United States)
PES Institute of Technology (India)
Ramesh Raskar, MIT Media Lab. (United States)


Published in SPIE Proceedings Vol. 9333:
Biomedical Applications of Light Scattering IX
Adam Wax; Vadim Backman, Editor(s)

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