KEYWORDS: Correlation function, 3D modeling, Hyperspectral imaging, 3D image processing, Algorithm development, Digital imaging, Image sensors, Detection and tracking algorithms, Visual process modeling, Sensors
We develop new algorithms based on multiband correlation models for
the recognition of hyperspectral textures in three dimensions. The
dependence of the observed texture of a material sample on viewing and illumination angles can have varying degrees of complexity. The
bidirectional texture function (BTF) describes the appearance of a
textured surface as a function of the illumination and viewing
directions. The lack of appropriate hyperspectral image sets has limited attempts to characterize the BTF for 3D hyperspectral textures. In this paper, we use the DIRSIG model to generate a set of hyperspectral images over ranges of illumination and viewing
angles in the 0.4 to 2.5 spectral region. We evaluate the performance
of our methods for recognizing three-dimensional hyperspectral textures under unknown illumination angle.
We introduce a representation for hyperspectral textures using unichrome and opponent features computed from Gabor filter outputs. The unichrome features are computed from the spectral bands independently while the opponent features combine information across different bands at different scales. Using a database of AVIRIS image regions, we evaluate the performance of the multiscale approach using opponent features for recognizing hyperspectral textures.
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