Constraining Object Features Using a Polarization Reflectance Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating surface normals from spherical stokes reflectance fields
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
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Existing polarization-based image understanding techniques use information only from reflected light. Apart from incandescent bodies thermally emitted light radiation from elements of a scene in the visible spectrum is insignificant. However, at longer wavelengths such as in the infrared thermal emission is typically quite prevalent from a number of scene elements of interest. FLIR imagery of both indoor and outdoor scenes reveals that many objects thermally emit a significant amount of radiation. Polarization from thermally emitting objects has been observed as long as 170 years ago from incandescent objects but since then there have only been a limited number of empirical investigations into this phenomenon. This paper presents a comprehensive model for explaining polarization of thermal emission from both rough and smooth surfaces, in agreement with empirical data, that can significantly enhance the image understanding of FLIR imagery. In particular it is possible to discern metal from dielectric materials under certain conditions, and from an accurate model for thermally emitted polarization it is possible to predictively model polarization signatures from CAD models of importance to automatic target recognition.