Added part about metrics

Signed-off-by: Jim Martens <github@2martens.de>

body_expose.tex

@@ -393,6 +393,20 @@ performance and computational performance under open set conditions
 using the SceneNet RGB-D data set with the MS COCO classes as
 "known" object classes.
 
+The computational performance is measured by the time in milliseconds
+every test run takes. Interesting are not the absolute numbers,
+as these vary from machine to machine and are influenced by a
+plethora of uncontrollable factors, but the relative difference
+between both approaches and if the difference is significant.
+Object detection performance is measured by precision, recall,
+F1-score, and an open set error. While the first three metrics are
+standard, the last is adapted from Miller et al. It is defined
+as the number of observations (for dropout sampling) or detections
+(for GPND) that pass the respective false positive test (entropy or
+novelty), fall on unknown objects (there are no overlapping ground
+truth objects with IoU \(\geq 0.5\) and a known true class label)
+and do not have a winning class label of "unknown".
+
 \subsection*{Technical Contribution}
 
 \chapter{Thesis as a project}