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Proceedings International Workshop on Mobile Mapping Technology
Li, Rongxing

7 A-1-3
Figure 4: Spike point (o) detection
A point is considered as a spike candidate if admi and
adm r are less than a threshold value, and if the grayscale
value of the point is considerably different from
G(x u ) andG(x ir ).
Only the candidate with the highest grayscale difference
from its neighbors will represent a spike of its region.
strong geometric features as straight lines, circles, comers, etc.,
but it is still possible to extract edges. Therefore, in this research,
edges are the most useful feature for rural scenes. We need the
descriptive information from edge points to use in the matching
process. Strong edges and low noise are preferred. Actually any
well-defined detectors based on the first derivative can satisfy
these objectives (Roberts, 1965), (Prewitt, 1970), (Davis, 1975).
Both gradient magnitude and direction which are very useful
information for matching can be obtained from these gradient
operators. Edges are extracted from 2D images. But only the
intersected points with epipolar lines are used in matching
3 Feature Matching
In order to convert the search problem into one dimension, the
images will be resampled into epipolar geometry. For any feature
found in one image, it is guaranteed that we need only look along
a single corresponding line in the other image in order to find a
match (discounting occlusions). With known parameters of
interior and exterior orientation (or at least relative orientation)
the epipolar resampling can be accomplished by generating two
fictitious pictures with parallel view directions.
MtslhitMj Besait
i «iti
Figure 5: Result from feature matching
In an image, spike points represent both point features such as
manhole covers and 2D features such as edges and straight lines.
Based on this interpretation, it is possible that some spike points
may be redundant with other features in a profile. The monotonic
constraint and the optimization policy of dynamic programming
in the feature matching process should take care of this situation.
Fig.4 illustrates some typical spike points in real intensity profiles
from the left and the right images.
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■■■■ ■»■■■■■■■
■■■ mmmmmmm ■■■
■«■■■■■■■a a
aaaaaaa aaa
■■aaaaafi aa mm
me a aaaa mmmmm
aaaaaaa aaaaa
a aaa afiraaa
The inevitable occlusions in large scale photographs lead to
requirement for robustness in the matching process, and further
argue for a global rather than a local cost function for the match
problem. In this matching application, the feature points on an
epipolar line of one image represent stages, whereas the feature
points of an epipolar line of the other image represent decision
variables for each stage. The descriptions of features are
employed to determine a cost function (Eq.8).
f(x n ) = min
c n (x n -i,dn)+ min (c l (x 0 ,d l ) + ‘
= min [c„ (*„_!, ) + /(*„_!)]
111 ■ II
Figure 6: An elevation profile on cost matrix
Edge Detection
Edge segments are commonly used as primitive features in stereo
matching. In the images of rural areas, it may be difficult to find
An ordering constraint of the matching process must be enforced.
It dictates that the optimum path chosen must be a monotonically
decreasing function.
The solutions may be different if the features from the right image
represent stages and the features from the left image represent
decision variables, instead of the reverse. This situation may
occur when the set of extracted features on the left image is
different from the set extracted from the right image. To handle
this situation, a revised strategy is introduced. The optimal paths
will be determined twice, considering each image of the pair as
the reference. The final solution would then be one that was
consistent with each of these provisional solutions.