In this paper, we propose a new fuzzy structural matching scheme for space stereo vision which is based on the fuzzy properties of regions of images and effectively reduces the computational burden in the following low level matching process. Three dimensional distance images of a space truss structural model are estimated using this scheme from stereo images sensed by Charge Coupled Device (CCD) TV cameras

Homma, Khozo

Iwata, Yoshitaka

Naka, Masao

Yamamoto, Hiromichi

English

NASA Technical Reports Server

A Fuzzy Structural Matching Scheme for Space Robotics Vision
Masao N AKA1), Hiromichi YAMAMOTO 1), Khozo HOMMA 1), Yoshitaka IWATA 2)
1)Computational Sciences Division, National Aerospace Laboratory N95" 23737
7-44-1 Jindaiji-higashi-machi, Chofu, Tokyo 182, JAPAN
Phone;(0422)47-5911, ext.2525,Fax;(0422)42-0566, Email;naka@nal.go.jp
2)Scientific and Technological Systems Dept.II, Fujitsu F.I.P.Inc.
4-14 Kamiyama-machi, Sibuya, Tokyo 150, JAPAN
Phone;(03)3481-4186, Fax,(03) 3481-9554
KEYWORDS AND PHRASES
Fuzzy logic, stereo matching, robot vision,
space robotics
ABSTRACT
In this paper, we propose a new fuzzy
structural matching scheme for space stereo
vision which is based on the fuzzy properties
of regions of images and effectively reduces
the computational burden in the following
low level matching process. Three dimen-
sional distance images of a space truss struc-
tural model are estimated using this scheme
from stereo images sensed by Charge Cou-
pled Device (CCD)TV cameras.
INTRODUCTION
The importance of advanced space
vision processing has increased in the space
station era for servicing, maintenance, repairs
and assembly by space teleoperated/robotics
systems. A stereo vision system with pas-
sive optical image sensors (CCD TV cam-
eras) is the simplest method of sensing and
perceiving the three dimensional (3-D) dis-
tances and attitude parameters of targets
objects using the stereo process of matching
and the principle of triangulation. Because
of the simplicity of the imaging equipment
and recent advance in the processing speed
of computers, this stereo vision system is ex-
pected to become a key technology for space
automation and robotics [Ref. 1,2]. However,
the techniques of stereo images processing
are insufficient so far, only the disparity
map of targets objects can be extracted
from their stereo images of uncooperative
(without special reflector or pattern marker)
targets objects in the space environment.
In this paper, we propose a new
fuzzy structural matching scheme and give
an example of its application for a space
truss structural model. This paper consists of
two parts. In the first part, a scheme for
higher level structural stereo matching algo-
rithms is discussed in terms of the fuzzy
based properties of labeled coarse regions of
stereo images. In the second part, the evalu-
ation experiments for the fuzzy stereo match-
ing scheme and fuzzy-based feature extrac-
tion are carried out using CCD images of
simple objects (a space structural model).
HIGHER LEVEL STRUCTURAL
MATCHING ALGORITHMS
Matching of the left and right im-
ages, the so-called correspondence problem,
is one of the most critical subjects in the field
of computer vision processing. Finding conju-
gate points of images can be performed in
several ways such as gray-level matching, lo-
cal correlation methods, edge matching, and
dynamic programming. However, these meth-
ods are less reliable and less efficient than
323
https://ntrs.nasa.gov/search.jsp?R=19950017317 2020-06-16T08:44:18+00:00Z
higher level structural methods as such seg-
ment- or region- based methods, because
these methods depend basically on point
matching.
A proposed matching algorithm is
fundamentally based on the matching bound-
ary-representation (B-rep) of stereo images as
proposed by F. Tomita et al [Ref. 3]. To
apply this method to the images of objects
resulting in space optical environments, we
modified this matching scheme using the
fuzzy set theory for region matching as a
higher level structural correspondence [Ref.
4]. An outline of the proposed matching
scheme with fuzzy properties of regions is as
follows. The properties of each region of
segmented images consist of perimeter L,
averaged gray level X, and aspect ratio T.
The similarity measure of the perimeter for
correspondence between the left region (i)
and the right region (j) of images is defmed
as follows.
_ 1 au<_m- Pd/'J)= ayzm- 
(1)
where AlO =abs (1 u - Is, )
1 tq m
m, - ×Z
MN t=l l=l
1 N M (m,-a,)2r"
crt MN t=_ 1=1
l ,,; perimeter of i - at region of left image
ltv; perimeter of j- tk region of right image
M, N; number of regions
The characteristic curve of membership func-
tion of this equation is shown in Figure 1.
Pt (i, j)
1.0
0. 5 .......................... i_
0.0 h lij
m-o rn m+o
Figure 1. Membership function of
Pc(perimeter)
This similar equation i s also used for other
324
property X. The similarity measure of the
aspect ratio T is defined as follows.
I At# _t# _<1
Pj(i,j) = 1 _t¢ > 1
[ At_
where Atct = tul tsj
tu; aspect ratio of
tsj: aspect ratio of
(2)
i - th regiom of left image
j- th regiom of fight image
The Total similarity measure of
region properties for correspondence between
the left and right regions of images, R(i,j), is
calculated as follows:
R(i,j) = Min(PL(i,j),Px(i,j),Pr(i,j)) (3)
and R(i,j) > threshold Re,
This higher level structural corre-
spondence (region based) is performed at the
first phase of matching computations. In this
way, the search space of the second phase for
low level structural matching (segment
based) can be narrowed. Total processing
flow of this hierarchical matching is shown
in Figure 2.
m
seglented images ]
fuzzy properties derivation of regions ]
Higher level structural matching using coarse regions ]
Lower level structural matching using segaents [
3-D distance information by triangulation ]
Figure 2. Hierarchical structural matching scheme
using B-rep of stereo images
LABORATORY EVALUATION
EXPERIMENTS
The evaluation experiments of the
fuzzy-based edge feature extraction and
stereo matching algoriflims were performed
using CCD images from models of target
objects.As a simplemodelof targetobjects,
weusea buildingblockmodelanda space
trussstructuralmodelsetuponanoptical
benchin the laboratory.Thelow contrast
and/or blurreddigital imagesof thetarget
objectsaregeneratedby aCCD TV camera
onboardax-y traversedeviceanda Sunwork
stationequippedwith an imagedigitizer.
A digital imageisrepresentedbyanarrayof
512X480(=MxN) pixels,and8-bit gray
levels.Imageprocessingflow for evaluation
experimentsis shownin Figure3.
stereo region edge detection,
images mapping labeling
 hist°'ra''lvalley points
fuzzy H3-D distance
structural
computation
matching
Figure 3. Image processing flow for estimation
of 3-D distance informations
Figure 4(a) shows an original stereo image of
a space truss structural model. The edge
feature detected and labeled image from the
original image are shown in Figure 4(b). A
3-D distance image of the object estimated
by the proposed matching scheme and
triangulation for labeled images is shown in
Figure 4(c). In this experiment, the number
of coarse regions correspondence was re-
duced to almost half by using a proposed
structural matching scheme.
CONCLUSIONS
As a part of the research on stereo vision
processing algorithms for aerospace applica-
tions, a stereo matching scheme based on the
fitzzy set theory has been studied. It has been
concluded that: (1) A higher level structural
matching scheme for labeled images is very
effective for reducing computational burden,
(2) Three dimensional distances are relatively
well estimated in a block model and a space
truss structural model.
REFERENCES
[ 1] Krishen, K., 1987. Vision technology/
algorithm for space robotic appfications,
NASA CP, 441-453.
[2] Montemerlo, M.D., 1990.Automation
and Robotics for Space Operation and
Planetary Exploration, Proc. of i-SAIRAS
'90.
[3] Tomita, F.,et al, 1988&latching
Boundary-representation of stereo images,
Trans. of IEICE,vol. J71 -D,no6.
[4] Naka, M., et al, 1993. Three
Dimensional Distance Information
techniques based on the concept of fuzzy set
for future space intelligent vision system,
Proc.of Asian Conference on Computer
Vision '93.
325
(left) (right)
Figure 4(a) Original stereo image of a space truss structural model
-.,...-....,.._/
(left) (right)
Figure 4(b) Segmentation images of a space truss structural model
/
Figure 4(c) 3-D distance image of a space truss structural model
326


A fuzzy structural matching scheme for space robotics vision

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