ABSTRACT:
results.
1. INTRODUCTION
Image matching is one of the key technologies to obtain 3D
space information from 2D airborne/spaceborne image by
digital photogrammetry. Along with the increasing use of new
digital sensors, it becomes more and more easier to acquire
large overlap digital images covering the same area, the multi-
view image matching approach has attracted wide interests in
both photogrammetry and computer vision. The traditional
image matching is restricted to the imaging abilities of stereo
sensors, which based on the matching of “single stereo-pair”,
and therefore is a challenging and “ill-posed” problem. Multi-
image matching translates the single-stereo mode matching in
image space to the multi-image matching by combining the
image space and object space. Comparing with the single
stereo-pair matching, multi-view image matching has
advantages in below: Firstly, it improves the reliability of
matching utilizing the redundant multi-view image information
to effectively solve mistake matching with repetition texture,
broken feature etc; Secondly, it maximally reduces the
information blind area of image, and solves the occlusion
problem in matching.
The algorithm of multi-view image matching can be divided
into two categories by matching modes: multi-image can be
matched in pairwise mode or in simultaneously multiple-
matching mode. The former is based on the traditionally
pairwise stereo matching. It separately matches the stereo pairs
one by one, integrates all of the matching results in object space,
and then obtains the correct matching results. For example,
Pateraki (2005) proposes the algorithm of adaptive multi-image
* Corresponding author: Jingxue Wang, Ph.D, E-mail: xiaoxuel861@163.com
A MULTI-VIEW IMAGE MATCHING METHOD FOR FEATURE POINTS BASED ON
THE MOVING Z-PLANE CONSTRAINT
Jingxue Wang *, Weidong Song, Fanqiu Bu
School of Geomatics, Liaoning Technical University, 123000, Fuxin, Liaoning, China
xiaoxue1861(@163.com; song wd@163.net
Commission [1/4
KEY WORDS: Multi-View Image Matching, Feature Points Matching, Moving Z-Plane Constraint, Grid Cell, Occlusion
Focusing on the serious occlusion problem in city images, this paper makes full use of the advantage of multi-view image matching,
and proposes a reliable multi-view image matching method based on the moving Z-Plane constraint. It supposes a fictitious plane in
the object space, and the plane is divided to regular grid cell (small plane element) by a certain interval (= image resolution). By
moving the plane to different elevation positions, this algorithm makes feature point projection ray in overall images intersect with
the plane, and constrains the candidate points by grid cells in the plane. Feature points which come from different images projection
ray in the same grid cell on the plane may be regarded as the matching candidates. It selects the images which matching candidate
points by gray similarity constraint to avoid the effect from occlusion image. According to the number of projection ray in the grid
cell, this algorithm adopts hierarchy matching strategy of "the best candidate will be matched in the first instant", and uses initial
matching results as constraint condition in the latter matching process. Finally, the validity of the algorithm proposed in this paper is
verified by the experiments using four UltraCamX (UCX) digital aerial images and the algorithm is shown to have reliable matching
matching (AIM), which is used for ADS40 image. It separately
establishes stereo pairs with reference image and searches
image at first. Then the algorithm makes quality check to
matching result in single stereo pair, and makes the least square
matching with multi-image according to corrected matching
result by the former matching. Yuan and Ming (2009) introduce
a multi-image matching algorithm by integrating image and
space information. This algorithm realizes the integration of the
matching results in each pairwise in the object space through
the multi-ray intersection with the function of gross error
detection by iterative predictive approach. Matching result in
pairwise mode is uncertain because it does not combine multi-
image redundancy information in the process of matching. So it
needs to be consistently constraint in object space or filter
method to be integrated into the matching results of multiple
stereo pairs in the object space, which simultaneously increases
the complexity of algorithm. Matching to all images
simultaneously by adopting object geometry constraint mode,
which obtains the corresponding point and the space coordinate
of the corresponding at the same time. Zhang (2005) and Zhang,
et al. (2008) propose the geometrically constrained so
correlation (GC?) algorithm, which chooses the nadir-viewing
image of linear array image as the reference image, and extracts
features from the reference image, and then searches the
corresponding feature in the search image. It comes through the
course of the from image space (reference image) to object
space and to image space (search image). Because it is restraint
to features extracted in the reference image, it does not adapt to
the image obtained by the center projective in the areas with
large gurgitation of the earth surface. If a certain space area 1s
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