AERIAL IMAGE MATCHING BASED ON ZERO-CROSSINGS 
Jia Zong 
Jin-Cheng Li 
Toni Schenk 
Department of Geodetic Science and Surveying 
The Ohio State University, Columbus, Ohio 43210-1247 
USA 
Commission III 
ABSTRACT 
One of the basic tasks in digital photogrammetry is to find conjugate points in a stereo pair and to reconstruct the 3-D 
object space (DEM). Edges play an important role in that they may indicate breaklines in the surface. We use the LoG 
operator to extract edges (zero-crossings). In this paper the problem of matching zero-crossings is addressed. Zero-crossings 
computed from one image are matched with area-based method. A hierarchical matching approach is adopted by the use of 
both, interpolated disparity maps at each level of the image pyramid, and knowledge from image analysis at very high level of 
image pyramid. The method is particularly suited for matching aerial images for the purpose of restructing surfaces of urban 
areas. 
KEY WORDS: Zero-crossing, Correspondent point, Figural Continuity, Disparity Interpolation, Image Analysis. 
1. INTRODUCTION 
One of the major research areas in digital photogrammetry is 
image matching for reconstructing the three-dimensional sur- 
face of the object space. This process involves a fundamental 
problem of stereo vision: to find corresponding points in an 
stereo-pair. Once correspoinding points are determined their 
three-dimensional positions can be easily computed, and the 
surface is obtained from matched points by interpolation. 
Two methods are commonly used in image matching: area- 
based image matching and feature-based image matching. 
Aera-based matching is predominantly used for the object 
space (DEM). Here, the corresponding points are found 
by comparing the gray levels of correponding areas (image 
patches) in a image stereo-pair. This approach is favored 
in photogrammetry because of its high accuracy potential. 
However, there are several critical factors that need special 
consideration in area-based matching. For example, 
e good approximations for the corresponding image 
patches are required 
e maíching in flat area or of sharp relief changes is ex- 
tremely hard and it produces bad results. Both cases 
usually occur in urban aerial images 
e recovering the surface, especially in urban areas, from 
randomly distributed matched points is difficult 
e the reliablity control of the matching is low 
e computations are intensive 
Some of these problems are avoided in feature-based match- 
ing. Here, properties (features) derived from the gray lev- 
els are matched, rather than gray levels themselves. This 
method usually proceeds in two steps, the first being a lo- 
cal similarity matching such as comparing the parameters of 
detected features, and the second being a global matching 
such as checking continuity constraints. Features detected 
144 
monocularly may differ and may include spurious data due 
to differences in reflectance which are not caused by the sur- 
face shape. This problem is quite acute in large-scale aerial 
images of urban areas. Another point to bear in mind is that 
matched features (e.g. edges) do not necessarily consist of 
conjugate points. In general, feature-based matching is more 
robust and less computationally intensive. But most impor- 
tant, matched features are more meaningful than randomly 
matched points if it comes to automatically analyzing image. 
The motivation for this research is to combine the merits of 
both area-based and feature-based matching methods. First, 
edges or zero-crossings (ZC) are detected as features. The 
edges are more likely to represent prominent features of the 
surface, such as breaklines. Instead of matching edges as en- 
tities as described in [Schenk et. al. 1991], here we match 
every point of an edge by correlation. A match is accepted 
if it satisfies epipolar geometry and figural continuity con- 
straints. This strategy proved to be quite successful [Li et. 
al. 1990]. In order to cope with urban areas where corre- 
lation must be applied with caution, we have modified the 
strategy by including a surface analysis step in the hierar- 
chical matching scheme. At each level of the image pyramid 
an interpolated disparity constraint map is generated which 
provides the necessary approximations for the next level of 
matching. Knowledge gained from previous levels is used 
to guide matching in the subsquent level of image pyramid. 
With this new strategy the success rate of matching aerial 
images of complex urban scenes is greatly improved. 
2. FEATURE EXTRACTION 
Detecting zero-crossings as features for matching was first 
proposed by Marr and Poggio [Marr and Poggio, 1979] on the 
basis of a computational theory on the human stereo vision. 
Mathematically, zero-crossings are obtained by applying the 
convolution operator V2G over the image f(z,y) as 
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