Trainable Weka Segmentation: added variation of information metric.

For now, it has methods to calculate the variation of information between 2D segmentations (images or stacks). This implementations uses the N^2 normalization and the foreground restriction. For 2D stacks, the metric value is the average of the variation of information through the slices.
Trainable Weka Segmentation: add javadoc comments.
Trainable Weka Segmentation: added methods to calculate an information theoretic analog of the F-score.
Trainable Weka Segmentation: added method to set verbose mode.
Trainable Weka Segmentation: move verbose flag to Metrics.java and create there a method to calculate the minimum metric value over a set of thresholds.
Trainable weka segmentation: correct typo.
Trainable Weka Segmentation: differentiate between classic variation of information and the foreground-restricted version we use in the 2D challenge.
Trainable Weka Segmentation: added log info.
Trainable Weka Segmentation: add javadoc comments.
Merge branch 'trainable-seg-gui' of https://github.com/fiji/fiji into trainable-seg-gui