(B) using a
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Fig. 6B is a
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Figure 5: (A) the original (RGB) image of the residential scene, (B) the Digital Surface Model (DSM), (C) the manually
measured CAD models of buildings, (D) the result of color classification alone. The pixels of the building class are shown (all
other pixels are black) (E) the result of classification for the MMO class and the projected DSM blobs (in grey), with NO
shown in black. The upper right house is not included in the DSM. (F) the result of building detection after combining the
spectral classification and the DSM blobs and refining the outline of the blobs by the use of edges.
Figure 6: (A) a cut-out from
the original image in Fig. 5A,
(B) The resulting contour
graph with all its contours
and end-points, (C) the
flanking regions with their
corresponding mean light-
ness attributes.
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B3. Vienna 1996
counted for. The photometric and chromatic attributes are
computed for each flanking region using the CIE L*a*b* color
space. The photometric region attributes are computed from
the lightness component L^ , whereas the chromatic region
attributes are derived from the a* and b" color components.
First, the mean lightness and its standard deviation are es-
timated by applying the Minimum Volume Ellipsoid (MVE)
estimator [Rousseeuw and Leroy 1987] on the L^ data. The
inliers in L^ are then used to robustly estimate the mean
vector and the scatter matrix for the chromatic components
(a*,b*). Again, the MVE estimator is used, however, with
two variables (a^, b^). The estimated scatter matrix of the
chromatic cluster is then diagonalized. The chromatic at-
tributes are thereby represented by the mean vector and the
two eigenvalues of the scatter matrix. In Fig. 6C we show the
mean lightness L* of each flanking region. The photometric
and chromatic region attributes are used to compute similar-
ity relations (next section) and in segment stereo matching
(section 6.1).
5.3 Contour Similarity Relations
Although geometric regularity is a major component in
the recognition of man-made structures, neglecting other
sources of information that corroborate the relatedness
among straight contours imposes unnecessary restrictions on
the approach. A popular means to relate pairs of straight
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