edu.stanford.rsl.konrad.utils
Class DoublePrecisionPointUtil

java.lang.Object
  extended by edu.stanford.rsl.konrad.utils.DoublePrecisionPointUtil

public abstract class DoublePrecisionPointUtil
extends java.lang.Object


Constructor Summary
DoublePrecisionPointUtil()
           
 
Method Summary
static java.util.ArrayList<PointND> extractCandidatePoints(ij.process.ImageProcessor houghSpace, double offset)
          Extract points from an ImageProcessor which exceed a certain value
static java.util.ArrayList<PointND> extractClusterCenter(java.util.ArrayList<PointND> pointList, double distance)
          Extracts cluster centers from an ordered List of points.
static PointND getGeometricCenter(java.util.ArrayList<PointND> list)
          Compute the geometric center of a set of points
 
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Constructor Detail

DoublePrecisionPointUtil

public DoublePrecisionPointUtil()
Method Detail

getGeometricCenter

public static PointND getGeometricCenter(java.util.ArrayList<PointND> list)
Compute the geometric center of a set of points

Parameters:
list - the set of points
Returns:
the geometric center

extractCandidatePoints

public static java.util.ArrayList<PointND> extractCandidatePoints(ij.process.ImageProcessor houghSpace,
                                                                  double offset)
Extract points from an ImageProcessor which exceed a certain value

Parameters:
houghSpace - the ImageProcessor
offset - the threshold for extraction
Returns:
the list of candidate points

extractClusterCenter

public static java.util.ArrayList<PointND> extractClusterCenter(java.util.ArrayList<PointND> pointList,
                                                                double distance)
Extracts cluster centers from an ordered List of points. Points must be ordered first with respect to x, then to y coordinate. Algorithm assumes that only one point may appear in the same row, i.e., all clusters must be separable via the y direction. A cluster center is then computed as the geometric center of the points in the same cluster. Algorithm is fast, but very restricted.

Parameters:
pointList - the list of candidate points
distance - the minimal distance between clusters
Returns:
the list of cluster centers