/**
  \addtogroup filters Module filters

  \section secFiltersPresentation Overview

The <b>pcl_filters</b> library contains outlier and noise removal mechanisms
for 3D point cloud data filtering applications.

An example of noise removal is presented in the figure below. Due to
measurement errors, certain datasets present a large number of <i>shadow
points</i>. This complicates the estimation of local point cloud 3D features.
Some of these outliers can be filtered by performing a statistical analysis on
each point's neighborhood, and trimming those which do not meet a certain
criteria. The sparse outlier removal implementation in PCL is based on the
computation of the distribution of point to neighbors distances in the input
dataset. For each point, the mean distance from it to all its neighbors is
computed. By assuming that the resulted distribution is Gaussian with a mean
and a standard deviation, all points whose mean distances are outside an
interval defined by the global distances mean and standard deviation can be
considered as outliers and trimmed from the dataset.

\image html http://www.pointclouds.org/assets/images/contents/documentation/filters_statistical_noise.png

	
	\section secFiltersHistory History
	- 1.0 current version
	
	\section secFiltersRequirements Requirements
	- common

*/
