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Proceedings Paper

Liquid-crystal polarization camera
Author(s): Lawrence B. Wolff; Todd A. Mancini
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Paper Abstract

We present a fully automated system which unites CCD camera technology with liquid crystal technology to create a polarization camera capable of sensing the polarization of reflected light from objects at pixel resolution. As polarization affords a more general physical description of light than does intensity, it can therefore provide a richer set of descriptive physical constraints for the understanding of images. Recently, it has been shown that polarization cues can be used to perform dielectric/metal material identification, specular and diffuse reflection component analysis, as well as complex image segmentations that would be immensely more complicated or even infeasible using intensity and color alone. Such analysis has so far been done with a linear polarizer mechanically rotated in front of a CCD camera. The full automation of resolving polarization components using liquid crystals not only affords an elegant application, but reduces the amount of optical distortion present in the wobbling of a mechanically rotating polarizer. In our system two twisted nematic liquid crystals are placed in front of a fixed polarizer placed in front of a CCD camera. The application of a series of electrical pulses to the liquid crystals in synchronization with the CCD camera video frame rate produces a controlled sequence of polarization component images that are stored and processed on Datacube boards. We present a scheme for mapping polarization states into hue, saturation, and intensity which is a very convenient representation for a polarization image. Our polarization camera outputs such a color image which can then be used in polarization- based vision methods. The unique vision understanding capabilities of our polarization camera system are demonstrated with experimental results showing polarization-based dielectric/metal material classification, specular reflection, and occluding contour segmentations in a fairly complex scene, and surface orientation constraints for object recognition.

Paper Details

Date Published: 1 November 1992
PDF: 12 pages
Proc. SPIE 1823, Machine Vision Applications, Architectures, and Systems Integration, (1 November 1992); doi: 10.1117/12.132068
Show Author Affiliations
Lawrence B. Wolff, Johns Hopkins Univ. (United States)
Todd A. Mancini, Johns Hopkins Univ. (United States)


Published in SPIE Proceedings Vol. 1823:
Machine Vision Applications, Architectures, and Systems Integration
Bruce G. Batchelor; Susan Snell Solomon; Frederick M. Waltz, Editor(s)

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