×

You are using an outdated browser that does not fully support the intranda viewer.
As a result, some pages may not be displayed correctly.

We recommend you use one of the following browsers:

Full text

Title
The 3rd ISPRS Workshop on Dynamic and Multi-Dimensional GIS & the 10th Annual Conference of CPGIS on Geoinformatics
Author
Chen, Jun

ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001
250
Since chained edge elements are not direct representation of
object’s shape, they must be approximated as line segments
(line segmentation), which are the fundamental information in
the following processes such as edge matching. The algorithm
of line segmentation is as followings.
Consider edge element with m nodes {n u ...n m ).
(i) Approximate line L, with node n, and n,. At initial i = 1 , j =
m.
(ii) Calculate distance d k (k = i+1,...J-1) between each n k and
4
(in) If any d k is greater than a pre-defined threshold dt,
substitute j-1 for j and go to step (i).
(iv) Register L, as line segmentation and substitute j for /.
(v) If i equals to j, process is finished, otherwise go to (i).
Line segments with length below a predefined threshold are
regarded as of low reliability and discarded.
4. EDGE MATCHING
The line segments obtained in the above process differ from
each other in stereo image pairs in many ways. To make line
segment usable for stereo matching process, the following
algorithms are proposed to select and modify reliable line
segment pairs.
We apply geometrical constraint and matching criterion of image
texture for detecting conjugate edge segments. Candidates of
conjugate edges are those of detected by processes of section
3. Fig.4 shows the flow of proposed process.
Fig.4 The Process Flow of Edge Matching
(1) Filtering with Geometric Constraint
In DPW system, the orientation parameters of an aerial image is
known, and can be used as geometric constraints for candidates
of conjugate points or line segments. In this step, it is assumed
that all edge segments that have the same epipolar line in the
other image are the candidates of conjugate pair. Combinations
of conjugate candidates are filtered with the following geometric
constraints.
(a) Edge candidates in the other image are supposed to exist
in a certain search width, which means that there is an upper
limit to parallax between conjugate edges.
(b) Conjugate edges have certain common section length
across epipolar lines. In other words, the distance in real
world between sharing section must exceed a specified
threshold.
(c) Altitude calculated with edges must be within a pre-defined
range. Anything out of this range will be rejected.
(d) Angle of elevation must be less than a specified threshold.
This condition gives priority to detection of edges that form
the top of buildings.
(2) Filtering with Image Similarity
In this phase, candidates of conjugate edges are further filtered
with evaluation of image similarity, which is based on the
correlation coefficient calculated with the templates shown in
Fig.5. The templates are chosen to be both sides of edge
segment between the overlapped range and the shape of
template is parallelogram, which is parallel to edge segment and
epipolar lines. The higher correlation coefficient of two templates
is adopted for evaluation criterion and the combination with
highest one is selected for candidate of conjugate edges.
(3) Expansion and Contraction
Normally, the conjugate lines are not of the same length. The
shorter edge of supposed conjugate edges is first extended to
that of the longer one. If the image similarity described above is
improved by this operation, expansion of edge is considered to
be valid. This operation is executed for all candidates of
conjugate edges. The next step is equalizing length of conjugate
edges (contraction process). In this stage specific edges in other
image have a possibility of referencing by multiple edges as a
candidate of conjugate edges, so in such a case, the above
processing is performed after copying those edges.
Edge Segment
Fig.5 The Layout of Template for Correlation Calculation
(4) Conjugate Edge Selection
As the last phase, conjugate edges are selected by the following
criterion
(a) An edge pair is adopted unconditionally when in the
assumed conjugate edge candidates refer to each other
as the highest edge and not referenced by other edges.
(b) If an edge A in a image is referenced by multiple edges B,
in another image, and edge A has connectable edges in
its neighborhood with a specified distance threshold, and
the same condition applies to a B of B„ the pair {A,B} is
selected.
(c) For cases similar to case (b), except that there are no
connectable edges, the edge before being copied in step
(3) is used, and the pair that gives the highest similarity
is selected.
5. CEM ENHANCED WITH EDGE CONSTRAINT
5.1 Coincidence Enhancement Method
Coincidence Enhancement is an extension of Hebb’s rule for
self-organized process in neural network modeling. This process
can be modeled by using the principle of competition and
consensus. CE model can realize smooth mapping between
input signals and output pattern. When take the left stereo
image as input and the right as output, this concept is applicable
to stereo matching.
The concept of stereo matching with CEM is illustrated in Fig.6.
In the competition phase, each pixel or local area in image A
tries to find an optimal poison in the search area. A shift vector
is formed by connecting the original position in A and the optimal
position in B. In consensus phase each shift vector is modified
with surrounding shift vectors in consensus area. These