Computational hair quality categorization in lower magnifications
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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.
Date issued
2015-03Department
Massachusetts Institute of Technology. Media Laboratory; Program in Media Arts and Sciences (Massachusetts Institute of Technology)Journal
Proceedings of SPIE--the Society of Photo-Optical Instrumentation Engineers
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Heshmat, Barmak et al. “Computational Hair Quality Categorization in Lower Magnifications.” Proceedings of SPIE, San Francisco, California, USA, 10 March, 2015. Vol. 9333., Edited by Adam Wax and Vadim Backman, SPIE, 2015. n.p. © 2015 SPIE
Version: Final published version
ISSN
0277-786X
1996-756X