7A-1-2
as length, orientation, closeness, etc. Much prior work has been
done on line extraction. For example, Boldt used the geometric
symbols grouping criteria and a hierarchical concept in his line
extraction algorithm (Boldt et al., 1989). Bums addressed the line
extraction problem by using the global organization of the
supporting line context (Bums et al., 1986). In this research, the
existing tool based on Bums’ algorithm has been using for
extracting straight line features. However only the intersections
between straight lines and epipolar lines are employed in
matching process. Fig. 1 illustrates the intersection between an
epipolar line and extracted lines overlaid on the original images.
Figure 1: The intersections of extracted lines and
the epipolar lines
• Use these attributes to refine (split and merge) clusters. All
refined clusters are called plateaus. The end points of
plateaus will be recorded along with their attributes.
Some useful properties of the plateau end points are recorded for
the feature matching process. Fig. 2 shows some examples of
plateau features extracted from the intensity profiles in images of
the Purdue University campus.
Spike Extraction
The spike is another one-dimensional feature utilized in this
research. It occurs predominately in images of an urban area. The
spike feature is described as a very short plateau, i.e., sharp peaks
or valleys, in the homogeneous region of an intensity profile.
They are extracted from the intensity profile in the epipolar space
as follows:
• For each pixel i along an intensity profile, compute the
average grayscale G(x t ) for the left neighbors and the right
neighbors (see Fig. 3).
G(x a )=- ZG(x k ) (4)
n k=i-p-n
Figure 2: Step/Plateau Extraction
Plateau Extraction
The plateau feature is defined here as a ID region with near
constant grayscale. It is particularly suited for built up areas or
highly urban areas. In manual DEM collection, an operator
would collect at least the end points of this feature, possibly also
intermediate points inside a plateau. The automatic plateau end
point extraction method is performed on an intensity profile along
an epipolar line as follows:
• Search on an intensity profile for
\G(x)-G(x-\))\< Threshold (1)
Where G(x) is the grayscale of pixel x. If the condition is
fulfilled the pixels will be recorded. Any contiguous pixels
meeting the criterion is called a cluster.
• Find attribues of clusters
AVGPL=
G(x)
st end - st
(2)
STDPL=
\_
e ^(G(x)-AVGPL) 2 2
s , end - st
(3)
1 i+p+n
G(x ir )=~ £G(x k ) (5)
n k=i+ p
• Determine the average deviation (adm) of the left neighbors
and the right neighbors of pixel i.
adm i=——; £ \G(x k -G(xa)i (6)
n * k=i-p-n
adm r =—i—- X \G(x k -G(x ir ))\ (7)
n 1 k=i+ p
, Where p is the expected width of a spike. And n is the
required distance of smooth neighbors.
j i i
Where st and end are a starting point and an ending point of Figure 3: Spike feature in an intensity profile
a cluster respectively.
