diff --git a/body.tex b/body.tex
index 06fed87..ece148a 100644
--- a/body.tex
+++ b/body.tex
@@ -128,7 +128,7 @@ of both for object detection in the open set conditions using
 the SSD network for object detection and the SceneNet RGB-D data set
 with MS COCO classes.
 
-\chapter{Background and Contribution}
+\chapter{Background}
 
 This chapter will begin with an overview over previous works
 in the field of this thesis. Afterwards the theoretical foundations
@@ -586,7 +586,23 @@ will explain how these data sets have been prepared.
 Afterwards the replication of the work of Miller et al. is
 outlined, followed by the implementation of the auto-encoder.
 
-\section{Design of Source Code}
+\section{Bayesian SSD for Novelty Detection}
+
+\subsection{Model Architecture}
+
+\subsection{Novelty Detection}
+
+\subsection{Implementation Details}
+
+\section{Auto-encoder for Novelty Detection}
+
+\subsection{Model Architecture}
+
+\subsection{Novelty Detection}
+
+\subsection{Implementation Details}
+
+\section{Software and Source Code Design}
 
 The source code of many published papers is either not available
 or seems like an afterthought: it is poorly documented, difficult
@@ -731,7 +747,13 @@ works very well for COCO as well, with one caveat: it is equally
 good for all classes, even when trained only on one. Novelty
 detection is out of the question under theses circumstances.
 
-\chapter{Results}
+\chapter{Experimental Setup and Results}
+
+\section{Data sets}
+
+\section{Experimental Setup}
+
+\section{Results}
 
 \chapter{Discussion}