Master Thesis - Fachbereich Informatik

68 CHAPTER 4. LENGTH MEASUREMENT APPROACH
The threshold τpeak is calculated dynamically based on the mean of P +
drv
P +
drv with
τpeak = αpeakP +
drv
denoted as
(4.9)
The factor αpeak indirectly relates to the number of peaks left to be further processed.
τpeak is also denoted as profile peak threshold. The goal is to remove as much peaks as
possible that do not belong to a tube’s boundary without eliminating any relevant peak.
If the images are almost uniform over larger regions as for black tubes, there are only
a few strong changes in intensity. Thus, P +
drv is expected to be quite low compared to
max(P +
) and the peaks belonging to the tube boundaries are conserved even for a larger
drv
αpeak. On the other hand, for transparent tubes the contrast between foreground and
background is lower. Hence, the distance between intensity changes due to background
clutter and those at the tube boundaries is much smaller. The choice of the right threshold
is more critical in this situation and αpeak has to be selected carefully. If it is too low,
too many peaks will survive the thresholding. Otherwise if it is too large, important
peaks will be eliminated as well. The profile peak threshold is closely related to the
detection sensitivity of the system as will be discussed in more detail in later sections.
More sophisticated calculations of τpeak considering the difference between maximum value
and mean or the median did not perform better.
Step 4: The x-coordinates of the remaining peaks defined as local maxima in Pthresh
arestoredinalistdenotedascandidate positions Ω in ascending order. NΩ indicates the
number of elements in Ω, i.e. the number of potential tube boundaries in an image.
4.4.3. Peak Evaluation
The process described in the previous section results in a number of candidate positions
that have to be evaluated since it is possible that there are more candidate positions
than the number of tube boundaries. This is due to the fact that the thresholding is
parametrized to avoid the elimination of relevant positions. The actual number of tube
boundaries indicating the current state as introduced in Section 4.2 is not known by now
and has to be extracted by applying model knowledge to the candidate positions.
Since only four of the nine possible states can be used for measuring, it is of interest to
know whether the current image matches one of these four states. If this is the case, it is
sufficient to localize the boundaries of the centered tube. Under the assumptions made in
Section 4.2 only one tube can be in the visual field of the camera completely at one time.
In the following, an approach reducing this problem to an iterative search for boundaries
that belong to a single foreground object is presented.
First, Ω is extended to Ω ′ by two more x-positions: x = 0 at the front and x = xmax
at the back of the list, where xmax is the largest possible x-coordinate in the profile.
Then, any segment s(i), defined as the region between two consecutive positions Ω(i) and
Ω(i + 1) , can be assigned to one of two classes in {BG, TUBE} representing background
and foreground respectively. In this way, the whole profile is partitioned into NΩ +1
segments if there are NΩ peaks.