New Statistical Algorithms for the Analysis of Mass - FU Berlin, FB MI ...

2.2. AN EXAMPLE 19
Figure 2.2.6: Two pictures of two distinct piles of Lego bricks taken by the
LEGOLAND team. They differ in one Lego brick. (Hint: it is the brick shown
in Figure 2.2.5.)
blurred. This extremely complicates the reliable detection of details such as
the borders of the single bricks or small differences. The two pictures in Figure
2.2.6 have the same differences as in Figure 2.2.5 - can you still find them ?
Recipe
Now, having an idea about the data we are working with (in this example)
we will give a small recipe to fulfill the task stated in the previous section.
We will later use the ideas sketched here to describe the actual algorithms
developed in this thesis. The steps are:
1. Find the bricks
2. Group the bricks
3. Analyze the groups
4. Check the feature quality
5. Compile a fingerprint
Step 1: Find the bricks
In each picture detect the borders of the individual Lego bricks (see Figure
2.2.7). For each brick found, write down its position and color in a list. Since
every picture can be processed independently from each other we can distribute
the single pictures to many different workers and collect the resulting lists
afterwards.
Step 2: Group the bricks
Use the lists of the previous step to identify and group together the same brick
in each picture. We assume that the same brick will appear at almost the same
position in each pile.
For example, bricks of group 1 have the position shown in Figure 2.2.8:
notice that although the two bricks of group 1 differ in color (the little white
dot alters the bricks overall color) they still belong to the same group. So,
a group is essentially a list of bricks having the same (or similar) positions
identified by a specific number.
Figure 2.2.7: Shown
are the borders found by
a border detection algorithm
applied to a picture
of Lego bricks.