The Fourier descriptors paradigm is a well-established approach for affine-invariant characterization of shape contours.
In the work presented here, we extend this method to images, and obtain a 2D Fourier representation that is invariant to
image rotation. The proposed technique retains phase uniqueness, and therefore structural image information is not lost.
Rotation-invariant phase coefficients were used to train a single multi-valued neuron (MVN) to recognize satellite and
human face images rotated by a wide range of angles. Experiments yielded 100% and 96.43% classification rate for each
data set, respectively. Recognition performance was additionally evaluated under effects of lossy JPEG compression and
additive Gaussian noise. Preliminary results show that the derived rotation-invariant features combined with the MVN
provide a promising scheme for efficient recognition of rotated images.
The use of empirical models for the extraction of optical and physiological properties from reflectance spectra is a
relatively new approach, as compared to other techniques such as those based on the diffusion theory and inverse Monte
Carlo algorithms. Empirical models are appealing for their ease of implementation and applicability to conditions for
which analytical models are limited. Thus far, however, empirical models have been limited to only a handful of specific
probe geometries. In this work, the relationship between reflectance and optical property values is explored for a wide
range of geometry and tissue conditions. The influence of variation in scattering phase function, and numerical aperture
of the optical fibers are investigated and incorporated into the empirical relationships for the first time. Reflectance data
used in this work was simulated using an improved Monte Carlo model designed to run on a graphics processing unit
(GPU). Improvements include a Modified Henyey-Greenstein and a Mie theory-based phase function in place of the
conventional Henyey-Greenstein phase function, and assignment of probe-specific reflectivity conditions to better model
the tissue-probe tip interface. These improvements are particularly important for probe geometries with small sourcedetector
separations. Probe geometries that offer the most stable relationships between reflectance and optical property
values, and hence, the best accuracy and reliability in extraction of physiological properties from tissue, are presented.