Uses of Class
trainableSegmentation.FeatureStack

Packages that use FeatureStack

Package	Description
trainableSegmentation

Uses of FeatureStack in trainableSegmentation

Methods in trainableSegmentation that return FeatureStack

Modifier and Type	Method and Description
FeatureStack	FeatureStackArray.get(int n) Get n-th stack in the array (remember n>=0)
FeatureStack	WekaSegmentation.getFeatureStack(int i) Get current feature stack

Methods in trainableSegmentation with parameters of type FeatureStack

Modifier and Type	Method and Description
boolean	WekaSegmentation.addBinaryData(ij.ImagePlus labelImage, FeatureStack featureStack, String className) Add instances to a specific class from a label (binary) image.
boolean	WekaSegmentation.addBinaryData(ij.ImagePlus labelImage, FeatureStack featureStack, String className1, String className2) Add instances to two classes from a label (binary) image.
boolean	WekaSegmentation.addLabeledData(ij.ImagePlus labelImage, FeatureStack featureStack) Add instances from a labeled image.
boolean	WekaSegmentation.addLabeledData(ij.process.ImageProcessor labelImage, FeatureStack featureStack, int[] classIndexToLabel, int numSamples) Add instances reading the pixel classes from a label image.
boolean	WekaSegmentation.addLabeledData(ij.process.ImageProcessor labelImage, FeatureStack featureStack, String[] classNames, int numSamples) Add instances reading the pixel classes from a label image.
boolean	WekaSegmentation.addRandomBalancedBinaryData(ij.process.ImageProcessor labelImage, FeatureStack featureStack, String whiteClassName, String blackClassName, int numSamples) Add instances to two classes from a label (binary) image in a random and balanced way.
boolean	WekaSegmentation.addRandomBalancedBinaryData(ij.process.ImageProcessor labelImage, ij.process.ImageProcessor mask, FeatureStack featureStack, String whiteClassName, String blackClassName, int numSamples) Add instances to two classes from a label (binary) image in a random and balanced way (with repetition).
boolean	WekaSegmentation.addRandomBalancedBinaryData(ij.process.ImageProcessor labelImage, ij.process.ImageProcessor mask, ij.process.ImageProcessor weights, FeatureStack featureStack, String whiteClassName, String blackClassName, int numSamples) Add instances to two classes from a label (binary) image in a random and balanced way (with repetition).
boolean	WekaSegmentation.addRandomBalancedLabeledData(ij.process.ImageProcessor labelImage, FeatureStack featureStack, int numSamples) Add instances from a labeled image in a random and balanced way.
boolean	WekaSegmentation.addRandomBalancedLabeledData(ij.process.ImageProcessor labelImage, FeatureStack featureStack, String[] classNames, int numSamples) Add instances reading the pixel classes from a label image in a random and balanced way.
boolean	WekaSegmentation.addRandomBalancedLabeledData(ij.process.ImageProcessor labelImage, int[] classIndexToLabel, FeatureStack featureStack, int numSamples) Add instances from a labeled image in a random and balanced way.
boolean	WekaSegmentation.addRandomData(ij.ImagePlus labelImage, FeatureStack featureStack, String whiteClassName, int numSamples) Add instances defined by a label (binary) image in a random way.
Callable<weka.core.Instances>	WekaSegmentation.createInstances(ArrayList<String> classNames, FeatureStack featureStack) Create instances of a feature stack (to be submitted to an Executor Service)
static void	WekaSegmentation.filterFeatureStackByList(ArrayList<String> featureNames, FeatureStack featureStack) Filter feature stack based on the list of feature names to use
void	FeatureStackArray.set(FeatureStack fs, int index) Set a member of the list
Callable<Boolean>	FeatureStackArray.updateFeatures(FeatureStack fs) Update features of a feature stack (to be submitted to an Executor Service)