# Full text: Proceedings International Workshop on Mobile Mapping Technology

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6.2 Aboveground Objects Extraction
REFERENCES
After shadow extraction, adjacent areas with high differences of
intensity means and small variances are called aboveground
object areas. We define a larger window that contains a pair of
such areas. A segmentation is performed once again to achieve
better boundaries. The above mentioned method is used to select
and link segments to form the boundaries.
6.3 Distinguishing Natural Objects and Man-Made Objects by
Shape Classification
Aboveground objects and their shadows have their specific
shapes, for example, natural objects such as trees and man-made
objects such as buildings and trucks. Natural objects often occur
in an irregular and complex area. In contrast, man-made objects
often have a regular contour shape, such as a box surface or
parallel edges. There are numerous methods to distinguish
between different shapes (Ballard and Brown 1982). In our
study, a contour shape is measured by the complexity of direction
change from pixel to pixel in a 3 x 3 image window. The.
direction change can be represented through Freeman chain
coding (Freeman 1974). Within a 3 x 3-image window, the
direction is coded as:
4 3 2
5 0 1
6 7 8
Suppose a set of chain codes is c 0 C,C 2 For any i,
0<i<n, if c M =C i =C I+1 , then the pixel direction at C { has
no change; otherwise there is a direction change at C ( . Moreover,
if ( 2,. . , then we can say direction
ma x 2Jc* +1 -c k \\<m
at C- has no change within a step size of m; otherwise there is a
direction change at C ( within a step size of m. Similar to what is
found in the fractal dimension of a shape calculation, the greater
the direction change, the more complex is the curve. Those
shapes with high complexity are classified as natural objects
while shapes with low complexity are classified as man-made
objects.
7. CONCLUSIONS AND ACKNOWLEDGEMENTS
This paper presents research results of feature extraction from
mobile mapping imagery sequence using geometric constraints
derived from GPS/INS and stereo models. The extracted features
are fed to object recognition models, for example, neural
networks.
The research was supported by National Science Foundation
(NSF project # CMS-9812783) and OSU Center for Mapping.
Mobile mapping data used in the paper are from Transmap Inc.
in Columbus, OH.
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