Master Thesis - Fachbereich Informatik

4.5. MEASURING POINT DETECTION 77
The results of the transparent tubes are crucial for the selection of an appropriate
edge detection approach used in this application, since due to the strong contrast the
detection of the black tube boundaries is uncritical with all tested methods. For both
tube types the edge detection results differ in detected orientation, edge elongation (i.e.
how precise an edge can be localized), or edge representation (signed/unsigned values,
floating point/binary, etc.).
Canny Edge Detector The Canny edge detector results in a skeletonized one pixel wide
response that precisely describes edges of arbitrary orientation. In this application the
main drawback of Canny’s approach is the importance of the threshold choice. As can
be seen in Table 4.2, different parameter sets yield very different results. If the upper
hysteresis threshold used as starting point for edge linking is low (e.g. 100) combined with
a lower second threshold (e.g. 50), too many background edges are detected as well. A
larger upper threshold (e.g. > 200) reduces the number of detected edge pixels, but also
eliminates parts of the tube edge. It is possible that it breaks up into parts. If the distance
between upper and lower threshold is large, it is likely that background and tube edges
are merged. In any case a threshold set working fine with one image can lead to very
poor results in another. The result of the Canny edge detector is a binary image where
non-edge pixels have a value of zero and edge pixels a value of one (or 255 in 8bit gray level
images). Binary contour algorithms can be applied to analyze chains of connected edge
pixels. As can be seen in the test images, depending on how many edge pixels survived
the thresholding, such analysis can be very complex and time-consuming. Gaps within
edges belonging to the tube boundary make this search even more complicated.
Sobel The Sobel operator approximates a Gaussian smoothing combined with differentiation.
It can be applied with respect to x- andy- direction. Accordingly to the filter
direction, vertical or horizontal edges are enhanced. Since the tube boundaries have a vertical
orientation, the SOBELX operator is an adequate choice in this application. Edges
are located at local extrema, i.e. local minima at bright-dark edges and local maxima
for dark-bright edges with respect to the gradient direction. A drawback is that also
the background pattern is dominantly vertical oriented, thus, background edges are also
detected. The intensity of an edge is related to the image contrast. Assuming a certain
contrast between tubes and background, a large amount of background clutter could be
removed by thresholding leaving only tube edges and edges due to high-contrast dirt particles.
However, this would lead to a similar approach like the Canny edge detector with
the drawbacks stated before.
Laplace The implementation used to test the Laplacian calculates the second-order derivative
in x- andy-direction using the Sobel operator and sums the results. The output is
an image of signed floating point values. Edges are located at the zero crossings between
strong peaks. The Laplacian is an anisotropic operator, thus, edges off all orientations are
detected equally. One drawback of this method is the sensitivity to noise. In the resulting
response there are many zero crossings. Compared to first-order derivatives, the edge criterion
is more complex. A pixel is an edge pixel if the closest neighbor in the direction of
the gradient is a local maximum while the opposite neighbor is a local minimum and both