Em
A NEW STEREO MATCHING APPROACH IN IMAGE/OBJECT DUAL SPACES
Yaonan Zhang
Centre for Computer Graphics and Mapping
Thijsseweg 11, Delft University of Technology
2629 JA Delft, The Netherlands
Commission V
ABSTRACT:
The stereo matching (or correspondence) remains one of permanent problems in Photogrammetry and Computer Vision.
This paper presents a new approach to solve the problem, which incorporates the image space based matching techniques
with the high level knowledge about the objects. The low-level processing (edge detection, feature extraction) and
candidate matching are carried out in image space, while the final matching is determined in object space as solving a
consistent labelling problem which results from the integration of candidate matching, high level constraints of objects
and other constraints of image matching. One of the innovative features in our approach lies in back-projecting (back
tracing) the line pairs from candidate matching into the object (scene) space, and combining all the constraints in a
unified process. We substitute the concept of "figure continuity" usually used in the image matching with the high level
knowledge from the object space.
KEYWORDS: Image processing, Image Matching, Line detection.
1. INTRODUCTION
The research on matching has been taken in computer
vision and photogrammetry society for quite a long
time. According to the space where the matching takes
place, the existing techniques for solving the matching
problem roughly fall into two categories: image space
based and object space based. In the image space based
matching, the primitives of one image are compared
with ones on the another image. Many solutions to the
matching have been proposed in the image space. The
methods vary with different choice of primitives: area-
based (intensity-based), feature-based and structure-
based (relational matching). Recently, several articles
are devoted to the object space based matching. This
method emerged originally from the task of
reconstructing digital terrain model from a pair of digital
images, independently developed by Wrobel' and
Helavz?, etc. Helava used the concept of "groundel" as
a unit in object space similar to the "pixel" in the image
space. The image intensities corresponding to each
groundel can be analytically computed, if all pertinent
geometric and radiometric parameters (including
groundel reflectance, etc.) are known. A least square
method is adopted to determine a set of unknown
quantities or improvements to their approximate values
used in the analytical prediction process.
Although the progress is undoubtedly made, most of the
algorithms are still task and domain dependent, many
parts of the problem still need full exploration of our
human intelligence. In solving this difficult problem, we
propose a novel approach which unify the techniques in
image and object space, combining the geometric
knowledge of scenes. The motivation behind this is that
534
a general solution for ill-posed problem such as
matching is to use additional constraints or knowledge
to restrict possible solution?. The existing constraints
used in the matching are the uniqueness, smoothness,
ordering, figure continuity and camera geometry
(epipolar geometry), etc. In this paper, we introduce a
new concept of "general geometric constraints of
scenes". In the problem of reconstructing digital terrain
model from digital image, the smooth constraints of
surface can be adequately assumed'?^^, But in the most
application of computer vision, industrial robot vision
and automated close-range photogrammetric system, the
scenes are full of lines, shapes and structures, it is
impossible to unify all the information in the object
space in a straight-forward way. The reason is that the
original digital image consists of only raster pixels
which can not directly provide much structural
information required heavily by later analysis. Without
low-level processing in the image space, it is practically
impossible to get more structural description of the
images. In our approach, we back-project (back trace)
the image primitives (from low-level processing) into the
object space, and carry the matching in the object space
combining the available knowledge from scene with
other constraints. We implement the line-based matching
in the image space in order to find the candidate line
pairs and then project the these line pairs into the object
space. The final matching became a consistent labelling
or constrained satisfaction problem. We use a relaxation
procedure to get the final lines in object space.
The main idea described in this paper has been
previously reported by author in 19917. This paper
includes the description on line detection and grouping,
back projection for horizontal image lines, as well as
more experimental results.
ext
prc
loc
fea
loc
inc
cor
res
Ed;
Sot
and
det:
glo
met
whi
low
bou