Forces, Points, and Surfaces

George Kamberov
Stevens Institute of Technology

Monday, January 27, 2:00PM
Lieb 3rd floor Conference Room
 

Abstract


This talk is focused on two basic computer vision problems: to what extent can we determine a surface and its properties from the surface normals (the Gauss map) and how to deal with the noise and lack of precision which seem to be inherently present in all current computer vision methods for extracting data about the Gauss map.

We will present a new theoretical method and experimental results for direct recovery of the principal shape descriptors (the curvatures and the principal curvature directions), and the surface itself by explicit integration of the Gauss map. The method does not rely on polygonal approximations, smoothing of the data, or model fitting. It is based on the observation that one can recover the surface restoring force from the Gauss map, and (i) applies to orientable surfaces of arbitrary topology (not necessarily closed); (ii) uses only first order linear differential equations; (iii) avoids the use of unstable computations; (iv) provides tools for filtering noise from the sampled data. The method is used for stable extraction of surfaces and surface shape invariants, in particular, in applications requiring accurate quantitative measurements.