Worked on report.

docs/data/diagrams.tex

@@ -253,13 +253,13 @@
                     edge[linefrom, dotted, bend left=65] node[left] {4} (gray);
             \end{tikzpicture}
         }
-        \caption{Two nested squares both listen to rotation gestures. The two
+        \caption{
+            Two nested squares both listen to rotation gestures. The two
             figures both show a touch object triggering an event, which is
-        delegated through the event area tree in the order indicated by the numbered
-        arrow labels. Normal arrows represent events, dotted arrows represent
-        gestures. Note that the dotted arrows only represent the path a gesture
-        would travel in the tree \emph{if triggered}, not an actual triggered
-        gesture.}
+            delegated through the event area tree in the order indicated by the
+            numbered arrow labels. Dotted arrows represent a flow of gestures,
+            regular arrows represent events.
+        }
         \label{fig:eventpropagation}
     \end{figure}
 }

docs/report.tex

@@ -381,7 +381,8 @@ detection for every new gesture-based application.
     area in the tree that contains the triggered event. That event area should
     be the first to delegate the event to its gesture detection components, and
     then propagate the event up in the tree to its ancestors. A gesture
-    detection component can stop the propagation of the event.
+    detection component can stop the propagation of the event by its
+    corresponding event area.
 
     An additional type of event propagation is ``immediate propagation'', which
     indicates propagation of an event from one gesture detection component to
@@ -422,20 +423,6 @@ detection for every new gesture-based application.
     complex gestures, like the writing of a character from the alphabet,
     require more advanced detection algorithms.
 
-    A way to detect these complex gestures based on a sequence of input events,
-    is with the use of machine learning methods, such as the Hidden Markov
-    Models \footnote{A Hidden Markov Model (HMM) is a statistical model without
-    a memory, it can be used to detect gestures based on the current input
-    state alone.} used for sign language detection by
-    \cite{conf/gw/RigollKE97}. A sequence of input states can be mapped to a
-    feature vector that is recognized as a particular gesture with a certain
-    probability. An advantage of using machine learning with respect to an
-    imperative programming style is that complex gestures can be described
-    without the use of explicit detection logic. For example, the detection of
-    the character `A' being written on the screen is difficult to implement
-    using an imperative programming style, while a trained machine learning
-    system can produce a match with relative ease.
-
     Sequences of events that are triggered by a multi-touch based surfaces are
     often of a manageable complexity. An imperative programming style is
     sufficient to detect many common gestures, like rotation and dragging. The
@@ -447,25 +434,40 @@ detection for every new gesture-based application.
     not managed well, the detection logic is prone to become chaotic and
     over-complex.
 
+    A way to detect more complex gestures based on a sequence of input events,
+    is with the use of machine learning methods, such as the Hidden Markov
+    Models \footnote{A Hidden Markov Model (HMM) is a statistical model without
+    a memory, it can be used to detect gestures based on the current input
+    state alone.} used for sign language detection by Gerhard Rigoll et al.
+    \cite{conf/gw/RigollKE97}. A sequence of input states can be mapped to a
+    feature vector that is recognized as a particular gesture with a certain
+    probability. An advantage of using machine learning with respect to an
+    imperative programming style is that complex gestures can be described
+    without the use of explicit detection logic, thus reducing code complexity.
+    For example, the detection of the character `A' being written on the screen
+    is difficult to implement using an imperative programming style, while a
+    trained machine learning system can produce a match with relative ease.
+
     To manage complexity and support multiple styles of gesture detection
     logic, the architecture has adopted the tracker-based design as described
-    by \cite{win7touch}. Different detection components are wrapped in separate
-    gesture tracking units, or \emph{gesture trackers}. The input of a gesture
-    tracker is provided by an event area in the form of events. Each gesture
-    detection component is wrapped in a gesture tracker with a fixed type of
-    input and output. Internally, the gesture tracker can adopt any programming
-    style. A character recognition component can use an HMM, whereas a tap
-    detection component defines a simple function that compares event
-    coordinates.
+    by Manoj Kumar \cite{win7touch}. Different detection components are wrapped
+    in separate gesture tracking units called \emph{gesture trackers}. The
+    input of a gesture tracker is provided by an event area in the form of
+    events.  Each gesture detection component is wrapped in a gesture tracker
+    with a fixed type of input and output. Internally, the gesture tracker can
+    adopt any programming style. A character recognition component can use an
+    HMM, whereas a tap detection component defines a simple function that
+    compares event coordinates.
 
     When a gesture tracker detects a gesture, this gesture is triggered in the
     corresponding event area. The event area then calls the callbacks which are
     bound to the gesture type by the application.
 
-    The use of gesture trackers as small detection units provides extendability
+    The use of gesture trackers as small detection units allows extendability
     of the architecture. A developer can write a custom gesture tracker and
     register it in the architecture. The tracker can use any type of detection
-    logic internally, as long as it translates events to gestures.
+    logic internally, as long as it translates low-level events to high-level
+    gestures.
 
     An example of a possible gesture tracker implementation is a
     ``transformation tracker'' that detects rotation, scaling and translation
@@ -498,7 +500,10 @@ detection for every new gesture-based application.
     An advantage of a daemon setup is that it can serve multiple applications
     at the same time. Alternatively, each application that uses gesture
     interaction would start its own instance of the architecture in a separate
-    process, which would be less efficient.
+    process, which would be less efficient. The network communication layer
+    also allows the architecture and a client application to run on separate
+    machines, thus distributing computational load. The other machine may even
+    use a different operating system.
 
     \section{Example usage}
     \label{sec:example}