Master Thesis - Fachbereich Informatik

4.4. TUBE LOCALIZATION 65
(a) (b)
Figure 4.12: (a) Control points (marked as crosses) are used to adjust the virtual calibration
grid of width w and height h to the underlying image data. (b) Gradient orientation φ at
each control point. Since the computed values are only an approximation, a narrow range of
orientations indicated by the gray cones around the ideal orientation is also seen as match.
have shown that it is possible to adjust the camera within an acceptable time to yield a
100% coverage in step one and a grid standard deviation of less than 0.3pixels. In this
case the camera is assumed to be adjusted good enough for accurate measurements.
4.4. Tube Localization
Since the system is intended to work without any external trigger (e.g. a light barrier)
that gives a signal whenever a tube is totally in the visual field of the camera, the first
step before further processing of a frame is to check whether there is a tube in the image
that can be measured or not. If there is no tube in the image or only in parts, this image
can be neglected. This decision has to be very fast and reliable.
4.4.1. Gray Level Profile
To classify an image into one of the states proposed in Section 4.2.2, an analysis of the intensity
profile along the x-axis is performed. Strong changes in intensity indicate potential
boundaries between tubes and background.
In ideal images as be seen in Figure 4.2, the localization of object boundaries is almost
trivial with standard edge detectors (see Section 2.3). In real image sequences, however,
there are many changes in intensity of different origin that do not belong to the boundaries
of a tube, e.g. caused by the background pattern (see Figure 3.8) or by dirt on the conveyor
belt. Furthermore, the printing on transparent tubes, visible in the image using back light
illumination, influences the intensity profile as will be seen later on.
The intensity profile ˆ Py of an image row y can be formally defined as