ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001
249
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highly reliable conjugate edges for general registration. Since in
the process of mapping between stereo image pairs do not
necessarily require conjugate edges, more edges that are not
strictly matched can be used for maintaining uniform parallax.
CE is one kind of neural network model and gives very unique
mapping process, which realizes nonlinear mapping from inputs
to outputs. In this study, this technique is applied to stereo
matching process, with the input and the output being the stereo
image pairs. Since CEM produces matching results less
sensitive to erroneous local minimum, it can reduce the
possibility of mismatching caused by occlusions or noises. In
order to reduce the effect of discontinuous shift vectors or
parallax changes, we use the edge information of high reliability
as constraints.
2.2 Process Flow
Fig. 1 shows the general process flow of our proposed
algorithms.
Fig. 1 The General Process Flow
In the first stage, edges are extracted using rectified stereo
image pairs, which are across epipolar lines. The processes
consist of several steps, such as edge enhancement, thinning,
tracing of enhanced edge image, segmentation, filtering by
geometrical constraint and template matching with the
consideration of edge boundaries [2]. We apply two different
types of edge operators for edge image enhancement and
compare the result to choose the more effective one. The
operators are SUSAN (Smallest Univalue Segment Assimilating
Nucleus) operator [3] and Haar’s wavelet transformation
respectively.
In the second stage, the edges extracted from stereo image
pairs are matched with each other to form conjugate edge pairs.
Geometric constraints from the orientation parameters, and
image similarity calculated from edge dependent template are
introduced to guarantee the high reliability of matching result.
The matched edges are further trimmed to the same shapes
according to their common section.
In the last stage, nonlinear matching process is performed. This
process is based on CEM. CEM realizes nonlinear normalization
at local area in the target system and acquiring of global
agreements by iterative computation. In this approach we
conceptually identify neuron's activity with matching or shifting
vector. This network model has two phases in optimizing
procedure of shifting vector. The first one is competition phase
in which shifting vectors are individually searched for optimal
displacement with certain constraints. The second one is
consensus phase in which shifting vectors are operated to have
local continuity of shifting vector or parallax. These two phases
are repeated to achieve local normalization with respect to the
edge matching results. We tried to extend CEM with the result of
edge matching.
3. EDGE DETECTION FROM RECTIFICATION IMAGE
To simplify the task of edge detection and refinement, stereo
image pairs are translated to rectified images using adequate
perspective projection, as long as proper tie points are specified.
This step includes photogrammetric operations such as
estimation of orientation parameters by relative orientation.
Rectification images provide a stereo model that has no y-
parallax. As a result, original 2 dimensional matching becomes
the equivalence of 1 dimensional matching (in x-direction,
corresponding to epipolar line), and only extraction of edges
across epipolar lines need to be considered.
The process flow of edge detection after rectification is shown in
Fig.2 [2]. In this study, two types of edge detection operators are
applied. One is the SUSAN operator proposed by Smith et al.
and the other is wavelet transformation. SUSAN operator is a
unique interest operator that pays attention to the variation of
regional brightness distribution in a local window. The brief
descriptions of each process are as follows.
Fig.2 The Process Flow of Edge Detection
(1) Pre-processing
As pre-processing, stereo image pairs are smoothed by 3 x 3
size median filter for noise reduction, and brightness or contrast
adjustment is applied to have almost the same brightness.
(2) Edge Enhancement
Two types of operators (SUSAN and wavelet) are used to
enhance edge component to get the candidates of building’s
structural edge. Haar’s wavelet is used here and operation is
applied in x-direction. The result of level-1 process is selected
for enhanced image. Binary edge images are also produced in
this stage. Eq.1 shows the basic formula of wavelet
transformation and Fig.3 shows Haar’s mother wavelet.
(WJ){b,a) = Eq. 1
where f(x) is input signal, (x) is mother wavelet function, a is
scale parameter and b is translation parameter.
1
1
-1
u
Fig.3 Haar’s Mother Function
(3) Edge Thinning
Applying mathematical morphology operation (dilation, erosion,
hit and miss etc.), binary edge components are locally
connected and thinned to trace edge segments.
(4) Edge Tracing
Edge tracing is performed by 8-direction neighbor connection
process. In this stage chained edge elements are extracted from
raster edge images.
(5) Edge Line Segmentation
