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Proceedings International Workshop on Mobile Mapping Technology
Li, Rongxing

Pakom Apaphant
School of Civil Engineering
Purdue University
James Bethel
School of Civil Engineering
Purdue University
bethel @ecn.purdue.edu
KEY WORDS: Dynamic Programming, Stereo Matching, Feature Extraction, Signal Matching, Object Surface Reconstruction.
An integrated image matching method for object surface reconstruction is developed. The proposed method is rigorously based on
photogrammetric principles incorporating some aspects of image understanding and computer vision. The matching problems are
addressed by the integration of signal and feature matching. The innovative strategy arises within the framework of global optimization
of the match function by dynamic programming. To increase computing speed, the algorithm is designed in such a way that it can be
implemented in either a parallel or a sequential computing system. To evaluate the performance of the algorithm, images of both urban
and non-urban scenes have been tested. The experiments have shown promising results using this approach.
Complicated stereo matching problems are found in large scale
images of urban scenes. Feature based matching (Medioni and
Nevada, 1985), (Ayache and Faverjon, 1987), (Dhond and
Aggarwal, 1989) has been used to address these problems.
However the extraction process may yield ambiguous or incorrect
solutions. Although successful feature matching may be achieved,
the information from matched features is still not sufficient to
reconstruct an entire object surface model. Signal based matching
(Forstner, 1984), (Lobonc, 1996) can also be used for solving
such a problem. This approach can work with images of rural
scenes. Its results, however, suffer when the assumption that the
terrain surface has smooth slope is violated. Many matching
methods tend to be based on only one of these approaches. This
trade-off is a dilemma in the matching process. In this research,
the problems are addressed by the integration of signal and
feature matching.
Difficulties in matching can be exacerbated by badly chosen
target search strategies. Using dynamic programming, object
surface generation can be reduced to the problem of an optimal
profile search from a cost matrix generated by a given stereo pair.
Dynamic programming requires that the problem must be viewed
as a multistage problem with interdependent variables. This
technique generally consists of three parts which are (1)
extracting the features to be matched and their descriptive
attributes (2) generating a correspondence cost matrix and (3)
searching for the optimal path in the cost matrix. Bernard
(Benard, 1984) and Ohta and Kanade (Ohta and Kanade, 1985)
can be regarded as the first groups among others who applied this
concept to the stereo matching problem. Most current dynamic
programming based matching techniques have been based on
their frameworks (Lloyd et al., 1987), (Liu and Skerjanc, 1993),
(Geiger et al., 1995), (Rojas et al., 1997). Dynamic programming
has been proven to be an effective though time consuming
strategy for stereo matching. Previous researchers applied this
technique only for feature matching. In this research, the
innovative strategy is the integration of feature and signal
matching within the framework of the global optimization.
Our matching process combines feature matching and signal
matching, and into an integrated algorithm. The sequence of steps
for feature matching would entail extracting features along
epipolar lines in the images, matching the features on the left and
right images. The conventional feature matching method by
dynamic programming was modified to make the algorithm more
robust. Then the feature coordinates are determined in the object
space. After that the signal matching technique is applied to
generate the elevation profiles. To increase the reliability of the
searching technique, 3D information obtained from feature
matching is integrated and used as constraints. The algorithm
was based on line following by dynamic programming. (Bethel et
al., 1998). Once all profiles in a model are determined, the
surface in the object space can finally be reconstructed. To
evaluate the performance of the algorithm, imageries of both
urban and rural scenes have been tested. The experiments have
shown promising results using this approach.
Several types of primitive features are extracted from images,
along with their positions in epipolar space. They are low level
features which contain useful information for the matching
process. These features are straight lines, edges, spikes, and
plateaus. They not only contain significant information but also
are not too difficult to extract. For all of these primitive features,
either the extraction is done directly in the ID epipolar space, or
the extraction is done in the 2D image space, and subsequently
intersected with the ID epipolar lines.
Straight line Extraction
Straight line features occur predominantly in built-up or urban
areas. This feature can contain much useful information for
matching. In addition to the edge location; orientation, adjacent
gray levels, and edge gradient are a few descriptive attributes.
Generally, a straight line extraction process starts from extracting
edge information, i.e. gradient magnitude and orientation. And
then some refinements may be applied for line linking and line
filtering. They are usually based on straight line parameters such