Bachelor Thesis - Computer Graphics Group

vector of fixed length, called the key point count. The algorithm, described
in [2], breaks the gesture down into a sequence of characteristic points that
define significant changes in the shape of the gesture. It works as follows:
• iterate through the list of points in the input sequence. Skip the first
and the last point
• remove the point from the result, if:
– the angle between the consequent segments is close to 180 ◦
– the distance from the last point kept is less than a given threshold
The remaining points now define the shape of the gesture. However, the number
of points can still be different from the requested amount. The polyline
has to be interpolated to achieve the given key point count, by splitting the
longest segments and joining the shortest ones. An overview of the algorithm
can be seen in figure 2.3.
Key point identification Interpolation
Figure 2.3: Overview of the preprocessing algorithm
Now when the input is normalized, we can proceed with the classification. Two
different gesture classifiers will be used. They both use the same preprocessing
algorithm. However, the input representation is different.
2.2 Neural network
A standard artificial neural network [4] with following properties will be used:
• three layers - input, hidden, output
• log-sigmoid activation function
• back-propagation training
• variable learning rate with momentum
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