=Paper=
{{Paper
|id=Vol-1458/E20_CRC7_Braun
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1458/E20_CRC7_Braun.pdf
|volume=Vol-1458
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==None==
<pdf width="1500px">https://ceur-ws.org/Vol-1458/E20_CRC7_Braun.pdf</pdf>
<pre>
 An Adaptive Grid Segmentation Algorithm for
  Mountain Silhouette Extraction from Images

              Daniel Braun, Michael Singhof, and Stefan Conrad

           Heinrich-Heine-Universität Düsseldorf, Institut für Informatik,
                   Universitätsstr. 1, 40225 Düsseldorf, Germany
                {braun,singhof,conrad}@cs.uni-duesseldorf.de

    Modern image sharing platforms such as instagram or flickr support an easy
publication of photos to the internet, thus leading to great numbers of available
photos. However, many of these images are not properly tagged so that there is
no notion of what they are showing. For the example of mountain recognition, it
is advisable to create reference silhouettes from digital elevation maps. Those are
matched with the silhouette extracted from a given image in order to recognise
the mountain. It is therefore necessary to obtain a very precise silhouette from
the query image.
    Our method utilises an adaptive grid segmentation algorithm that extracts
the silhouette from a query image. This approach first overlays the image with a
grid, with defined grid element spacing, and calculates, through a classification
step, for every grid point a score for the probability to belong to the sky segment
of the image. Afterwards, the algorithm segments the image with a seed growing
algorithm, starting at the grid points with the highest score, which are addition-
ally connected to an high score point in the top row of the image, due to the
assumption, that the sky will be localised in the upper part of the image. Having
the image binary segmented the algorithm extracts the transition between the
two segments as initial silhouette.
    The silhouette extracted by this approach may, however, include outliers that
are either artefacts, for example as result of segmentation errors, or obstacles
like trees in front of the mountain’s silhouette. Our approach tries to find these
outliers during an outlier detection step and afterwards to classify those into the
mentioned classes. If an obstacle is detected, it is removed from the silhouette
by replacing it by a straight. If an artefact is detected this gets reported to the
segmentation step of our algorithm. There, with changed parameters, for the grid
points located around the artefact, for edge detection, we try to find a better
segmentation for the part of the silhouette the outlier appeared in. These steps
are repeated until we end with a silhouette free of outliers and obstacles.
    First experiments show that we reach a median average deviation of 1.51
pixels to the reference silhouettes. Hereby, we measure the deviation of each
pixel of one silhouette extracted by our approach to the corresponding pixel of
the reference silhouette.
  Copyright c 2015 by the paper’s authors. Copying permitted only for private and
  academic purposes. In: R. Bergmann, S. Görg, G. Müller (Eds.): Proceedings of
  the LWA 2015 Workshops: KDML, FGWM, IR, and FGDB. Trier, Germany, 7.-9.
  October 2015, published at http://ceur-ws.org


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</pre>