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Fig. 6. Attain horizontal borders of the box
In the third approach that can be seen in fig. 7 advantage
is taken of the texture of the level gauge. Besides the metal
box and the liquid column in the middle it consists of ten big
screws arranged in two vertical lines. A set of screw images
is used for template matching and detecting possible screws
in the scene. The concept is to deliberately look for more
than ten screws and then classify them into so called good
screws, that do belong to the gauge, and bad screws, that do
not. This is done by creating a new black image, where the
center of every found screw is marked as a white pixel. If a
pixel is alreadywhite, the one below is made white instead to
make it count. Afterwards Hough line detection is applied in
this binary image to find lines of screws. The two lines with
most participating pixels are used to finally determine the
position of the box. Knowledge about the maximum amount
of screws or about their similar vertical distance can be used
to optimize the result.
Fig. 7. Attain the position of the outer box by using template matching to
find screws of the box. More than the existing 10 screws are to be found
to then form lines of screws.
If theheightof thebox isgiven, the fourthapproachcanbe
applied. Similar to the third approach white dots are created
in a black image for found screw templates. However, the
white dots for found screws are made bigger and compared
to an image created in the algorithm, consisting out of ten
big white filled dots. Those are placed exactly on the spots,
where a level gauge of the given size has located its screws.
The comparison is done by sliding the artificial ten dot image over the scene image and adding one to the correlation
variable for eachpixel that is white in both images. Thepoint
with the highest correlation marks the estimated position of
the level gauge’s outer box.
To combine the strengths of the algorithms mentioned
above, the fifth option is based on line detection of box bor-
ders and template matching with screws. Instead of getting
just the two best vertical lines, more of them are to be found
implementing a Canny edge detector and Hough transform.
Next screw templates are found in the scene. Lines, as well
as screws are then graded identifying their relative horizontal
distances. There have to be a certain number of screws in
the vicinity of a line, to mark both of them as good. Fig.
7 shows, how finally good screws and lines are marked in
green, other discarded ones in blue and bad ones in red.
Fig. 8. Combine the use of template screws and line detection to optimize
the result.
B. Cut-out of Liquid Column
The combination of the box detection methods above lays
the foundation for localizing the inner liquid column and
cutting it out. As the result image of the box detection
contains just the box, the position of the inner liquid column
is acquired using height and width of the column in respect
to the box size. As the appearance of the box is known, the
outer edges of the column are searched for in a specific area,
to get detailed borders.
C. Level Detection
Having acquired and cut out the liquid column, the level
is obtained. Allthough there are many different kinds of
disturbances when detecting the liquid level, reflections and
translucence are the ones affecting the reading the most, as
described infig.2.Horizontal reflectionsof the sunornearby
objects create horizontal lines, that often are even more
prominent than the real water level. Pipes or other objects
behind the liquid level gauge also create horizontal gradients
in the intensity image of the liquid column that is used to
obtain the correct level. To overcome these disturbances,
images of the gauge are taken from different heights.
As the mobile robot’s arm is restricted to five degrees of
freedom, the normal pose of the camera mounted on the arm
has to be altered. To achieve readings from different heights,
103
Proceedings of the OAGM&ARW Joint Workshop
Vision, Automation and Robotics
- Title
- Proceedings of the OAGM&ARW Joint Workshop
- Subtitle
- Vision, Automation and Robotics
- Authors
- Peter M. Roth
- Markus Vincze
- Wilfried Kubinger
- Andreas Müller
- Bernhard Blaschitz
- Svorad Stolc
- Publisher
- Verlag der Technischen Universität Graz
- Location
- Wien
- Date
- 2017
- Language
- English
- License
- CC BY 4.0
- ISBN
- 978-3-85125-524-9
- Size
- 21.0 x 29.7 cm
- Pages
- 188
- Keywords
- Tagungsband
- Categories
- International
- Tagungsbände