Detecting, modeling and rendering complex configurations of curvilinear features

Candidate: Evgueni Parilov

Advisor: Denis Zorin

Curvilinear features allow one to represent a variety of real world regular
patterns like honeycomb tiling as well as very complicated
random patterns like networks of furrows on the surface of the human skin. We
have developed a set of methods and new data
representations for solving key problems related to curvilinear features, which
include robust detection of intricate networks of curvilinear
features from digital images, GPU-based sharp rendering of fields with
curvilinear features, and a parametric synthesis approach to generate
systems of curvilinear features with desirable local configurations and global
control.

The existing edge-detection techniques may underperform in the presence of
noise, usually do not link the detected edge points into chains, often fail on
complex structures, heavily depend on
initial guess, and assume significant manual phase. We have developed a
technique based on active contours, or snakes, which avoids manual
initial positioning of the snakes and can detect large networks of curves with
complex junctions without user guidance.

The standard bilinear interpolation of piecewise continuous fields results in
unwanted smoothing along the curvilinear discontinuities.
Spatially varying features can be best represented as a function of the
distance to the discontinuity curves and its gradient.
We have developed a real-time, GPU-based method for unsigned distance function
field and its gradient field interpolation which
preserves discontinuity feature curves, represented by quadratic Bezier curves,
with minimal restriction on their topology.

Detail features are very important visual clues which make computer-generated
imagery look less artificial.
Instead of using sample-based synthesis technique which lacks user control on
features usually producing gaps in features or breaking
feature coherency, we have explored an alternative approach of generating
features using random fibre processes. We have developed
a Gibbs-type random process of linear fibres based on local fibre interactions.
It allows generating non-stationary curvilinear networks
with some degree of regularity, and provides an intuitive set of parameters
which directly defines fibre local configurations and global
pattern of fibres.

For random systems of linear fibres which approximately form two orthogonal
dominant orientation fields, we have adapted
a streamline placement algorithm which converts such systems into overlapping
random sets of coherent smooth curves.