International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B1, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
It is because the algorithm constitutes the rule stating it is
designed for data having height as shown in equation 3.
HAA 3 isDesignedFor.Data3D T1 Data3D.hasHeight.(»0) (3)
The execution of the algorithm detects prominent geometries of
dominant and simple objects in the scene. Qualification follows
detections. This is carried out through extended SWRL.
Examples can be seen in equation 1 and 2.
As stated, three possible qualifications are possible:
unambiguous, ambiguous and unknown. For simplicity we
carry forward this discussion with the first case where the
objects are qualified distinctly. The WiDOP platform utilizes
the semantic rules (defined through property restrictions) to
infer the algorithm or algorithmic sequence for verification. We
illustrate this with the two prominent objects in the DB scene:
Mast (poles carrying cables for powering trains) and Signal.
Figure 9 presents point cloud subsets for them.
The classes of types DomainConcept is related to that of
Geometry through specialized properties of hasGeometry (see
fig 2).
DCE 3 hasGeometry.Geometry (4)
(a) (b)
Figure 9. Point cloud sets (a) Signal (b) Mast
The specializations of class Geometry are possible geometry
types (fig. 5a). For instance Line3D is a type of _3D which is a
specialization of the class Geometry.
Geometry E_3DE Line_3D (5)
Putting together equation 4 and 5, we can conclude that Mast
has Line 3D. Furthermore, we can also say Mast has Line 3D
with dense, linearly arranged points (we term them as thick
lines for simplicity) as shown in equation 6, and Signal has
Line 3D with a low density of points (called thin lines for
simplicity), as shown in equation 7. Here thick and thin are
characteristics of the line. These characteristics (termed
hasChar in DL equations) are helpful in determining the input
parameters for the algorithms. It will be discussed later but for
now we present how ASM uses these simple rules in
algorithmic selection.
Mast = 3 hasLine3D.Line3D 1 Line3D.hasChar.{ Thick} (6)
Signal = 3 hasLine3D.Line3D MN Line3D.hasChar.{ Thin} (7)
94
The reasoning engine of the underlying ASM infers the rules
against the rules within sub-classes of Algorithms. Algorithm
LineDetectionin3DbyRANSAC is recommended, considering
that it is designed for 3D lines.
LineDetectionin3DbyRANSAC = 3 isDesignedFor.Line3D (8)
The reasoning engine inside ASM implements the same
principle to infer algorithms for other knowledge domains. We
have implemented it against the data knowledge under class
Data. Presuming that the standard deviation of a dataset
establishes a noise value for that dataset, ASM then infers the
algorithms best suited for datasets containing noise. It shows
the use of universal knowledge through combining different
knowledge domains (related to the scene, to classes of objects,
to instruments and so on), allowing the ASM to interact with
them. This interaction helps in providing answers for detecting
objects in extreme situations. However, it is necessary to define
appropriate rules to determine the usage of these knowledge
domains. Likewise, the underlying knowledge schema (fig. 2)
provides freedom in choosing its data source. We use a 3D
point cloud from the DB scene for our case, however it is also
possible to use images or other data formats.
3.2. Simulation Knowledge
Algorithms behave differently in different situations, for
instance reflecting differences in geometry or data. Even two
characteristics of the same geometry might need to be
addressed in the detection algorithm. As shown in Equation 6
and 7, the ASM chooses LineDetection3DbyRANSAC for
detecting the geometries, but using the same parameters for
both cases might not yield best results. In principle, it should
use different parameters for different point densities of the
linear structure to capture most of the points within the linear
structure (fig. 10). We thus need a higher radius value for thick
lines and a lower radius value for the thin.
Dist thre: =0.1
Figure 10. Cylinder radius for detection
This is exactly the intention behind obtaining and modeling the
simulation knowledge into the KB. Each observation of the
execution of an individual algorithm is induced in the KB.
These simulations are based on the results as they are tested
against different data, geometries, and other characteristics. The
clear benefit is that in above given situations for Mast and
Signal, the ASM of the WiDOP platform selects a different
radius threshold for the LineDetectionin3DbyRANSAC
algorithm. Furthermore, the ASM can evaluate the rules defined
by the scene to select different algorithms for different cases.
Instead of LineDetectionin3DbyRANSAC, ASM recommends
2DHoughTransformation for example, if the detection process
uses images or any 2D data as source data.
3.3. The Result
Our approach was tested with a 500 m long 3D point cloud of
the Nuremberg main station (Niirnberg Hbf). The KB consists
of tl
coul
Tab
scer
105
ann
curr
sets
afte
Tab
moc
sho
The
is S
moc
in tl
The
suit
pre
bee
the
bas
that
suit
pro
inte
kno
add
Thi
rese
edu
the
Chr
Ref
Ang
A,
segr
Patt
Die;
Bec
Lan
ref/