author = {Clarenz, U. and Rumpf, M. and Telea, A.},
  title = {Robust Feature Detection and Local Classification for Surfaces based
	on Moment Analysis},
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  year = {2004},
  volume = {10},
  pages = {516--524},
  number = {5},
  abstract = {The stable local classification of discrete surfaces with respect
	to features such as edges and corners or concave and convex regions
	respectively is as well difficult as indispensable for many surface
	processing applications. Usually the feature detection is done via
	a local curvature analysis. If concerned with large triangular and
	irregular grids, e.~g. generated via a marching cube algorithm, the
	detectors are tedious to treat and a robust classification is hard
	to achieve. Here a local classification method on surfaces is presented
	which avoids the evaluation of discretized curvature quantities.
	Moreover, it provides an indicator for smoothness of a given discrete
	surface and comes together with a built-in multi scale. The proposed
	classification tool is based on local zero and first moments on the
	discrete surface. The corresponding integral quantities are stable
	to compute and they give less noisy results compared to discrete
	curvature quantities. The stencil width for the integration of the
	moments turns out to the scale parameter.\par Perspective surface
	processing applications are the segmentation on surfaces, surface
	comparison and matching and surface modeling. Here a method for feature
	preserving fairing of surfaces is discussed to underline the applicability
	of the presented approach.},
  pdf = { 1}