Master Thesis - Fachbereich Informatik

4.4. TUBE LOCALIZATION 73
Global Regional
Total number: 467 467
Measurable: 353 414
Average PTM: 5.98 7.01
Table 4.1: Comparison of the global and regional threshold used for classification in the
profile analysis. The table shows the number of images that have been correctly detected
as measurable compared to the total number, as well as the average number of per tube
measurements (PTM). Using the regional threshold increases the number of measurements
significantly.
Figure 4.16: Ghost effect: If the parameters of the profile analysis are too sensitive, darker
parts on the conveyor (e.g. due to dirt or background structure) can be wrongly classified as
atube.
completely in the image. The sequence has been analyzed once with the global threshold
and once with the regional threshold. All other parameters have been constant. The
results can be found in Table 4.1. In this context it is important to understand that the
term measurable is related to a single image. It does not mean if the system fails to detect
a tube in one image that the tube can pass the visual field of the camera undetected. This
occurs only if it is not measured in all images that include this tube what is very unlikely.
The experiment shows the average number of measurements per tube can be increased
by approximately one if using the regional instead of the global mean as threshold for the
tube classification. Particularly situations as in Figure 4.15(a) can be prevented.
The reason why none of the two method has detected all measurable frames is due to
other parameters for example a too little contrast between a tube and the background.
The dynamic threshold τpeak as introduced before defines the strongest peaks of the profile
derivative. If it is too large, low contrast tube edges may not be detected. On the other
hand, if it is too low, darker regions in the background may be wrongly classified as
foreground. This leads to ghost effects, i.e. the system detects a tube where actually
no tube is as can be seen in Figure 4.16. Therefore, the weighting factor αpeak of τpeak
(see Equation 4.9) must be adjusted in the teach-in step to the smallest value that does
not produce ghost effects if inspecting an empty, moving conveyor belt. Obviously, the
compromise gets larger with an increasing amount of dirt.
Merging Segments In Figure 4.14(a), one can find two more segments than needed to
represent the actual state entering + leaving. Two strong peaks on the right that are due
to a dark dirt spot on the conveyor belt have not been eliminated by the thresholding.
However, the corresponding segments are correctly classified as background leading to
three consecutive background segments which could be merged to one large segment.
In general, once all segments s(i) are classified the goal is to iteratively merge neighboring
segments of the same class and to eliminate foreground segments that do not qualify