Master Thesis - Fachbereich Informatik

4.5. MEASURING POINT DETECTION 75
for measuring. An overview of the algorithm is shown in Listing 4.1. A size filter operation,
which can be parametrized with respect to the given target length, is used to remove
too small foreground segments (e.g. caused by dirt on the conveyor belt).
The output of the algorithm is either one large background segment (i.e. all foreground
segments have been removed if existed since they did not fulfill the criteria) or three
segments in the form BG-TUBE-BG. In the later case, the peaks belonging to the left
and right boundary of the remaining foreground segment are finally verified with respect
to the sign of the derivative. With the derivative operator used, the position of the left
boundary must result in a negative first-order derivative value (bright-dark edge) and the
right boundary in a positive value (dark-bright edge). If the predicted tube boundaries
are consistent with this last criterion, they are used to define two local ROIs of width
WROI as starting point for a more precise detection of the measuring points. The local
ROI height is defined over the distance between the two guide bars.
ThemergingofthesegmentsisalinearoperationinthecomplexityofO(NΩ). Since it
is only allowed to reclassify a former foreground segment into background in this procedure
and never vice versa, Step2 of the algorithm is repeated only once if at all. Hence, the
algorithm terminates for sure.
If all segment are classified as TUBE in the first step, an error is returned. This error
indicates the presence of state full (See Figure 4.2(i)). The reason can be due to a too
small field of view of the camera or to a missing spacing between consecutive tubes. In
any case it is not possible to perform a measuring. Since this state is critical compared to
other states that can not be used for measuring, it is important to detect this situation.
In practice, if this situation occurs an alert must be produced.
4.5. Measuring Point Detection
The previous sections described a fast method to distinguish whether a frame is useful or
not. If a measuring is possible, two regions around the potential left and right boundary
of a tube to be measured are the output of this first step. In the following, the exact tube
boundaries have to be detected with subpixel accuracy.
4.5.1. Edge Enhancement
As introduced in Section 2.3 there is a large number of approaches for edge detection. Four
common methods including the Sobel operator, Laplace operator, Canny edge detector [13]
and a steerable filter edge detector based on the derivative of a parametrized Gaussian
have been applied to test images. The results can be found in Table 4.2. It includes
experiments with two transparent tubes (left boundary) of the same sequence and one
black tube boundary. All tubes have a inner diameter of 8mm. The difference in size
between the transparent and black tubes is due to a different camera-object distance.
As can be seen the edge of the transparent tubes can differ in brightness, contrast and
background pattern between frames.
The goal was to find an edge detection operation that adequately extracts the tube
boundaries under the presence of background structure and noise, and which is computational
inexpensive in addition.