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G.L. Foresti; F.A. Pellegrino;
IEEE Transactions on applications and Reviews,
Volume 34, Issue 3, Aug. 2004 Page(s):325 - 333
PPT製作： （100%）
指導教授：謝銘原
學
生：柯俊毅
學
號：M9820212
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Introduction
System description
Automatic camera regulation
Object classification
Picking point extraction
Experimental results
Conclusion
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This paper describes a vision-based system
that is able to automatically recognize
deformable objects, to estimate their pose,
and to select suitable picking points.
Fig. 1. General scheme of the automatic handling system.
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Fig. 2. Functional diagram of the vision system.
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The goal of the camera regulation module is
to control the internal parameters of the
camera (e.g., focus and aperture) in an
automatic way, to allow acquisition of highquality images.
I  x, y   S ( x, y )  I x2  I 2y
2
 ix  I  x, y    i y  I  x, y  
N
N
TN   S  x, y  ,
2
2
for S  x, y   Th
1
(2)
x 1 y 1
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Fig. 4. Example of focus regulation: (a) original image acquired with a wrong
focus (focus step = 37) , and (b) the same image acquired with the best focus
parameter (focus step = 44).
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SOMs are a special class of ANNS
based on competitive learning.
Fig. 5. General
architecture of the
applied neural
classifier.
SOM1 acts as
feature extractor,
while SOM2 is the
real classifier.
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The output of the hierarchical
SOM is characterized by
several nonconnected regions
of interest ( i.e., fur regions).
Fig. 6. Typical classification result obtained with n = 5(size of the macro-pixel),
N = 6 and K = 5 (number of problem classes).
(a) Original image, (b) output of the classifier. (c) pixels recognized as fur,
(d) the detected connected components, (e) skeleton function of the detected blobs, and (f)
branch points
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Fig. 7. (a) Four valid picking points extracted from the original image
(b) three valid picking points extracted from an image acquired in real
operating conditions.
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Fig. 8. (a) the
original image
containing 15 furs
placed in a random
way on a flat
surface. (b) the
output image,
where five classes
have been found. (c)
the original image
with the detected
fur regions. (d)
three valid picking
points extracted
from the original
image.
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A. Camera Calibration
B. Recognition and Handling of Furs
C. Performance Evaluation
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The aim of the vision system is to give
to the robot arm the world coordinates
of a correct picking point.
B. Recognition and Handling of Furs
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Fig. 9. Further examples of the picking point procedure. Images where (a)-(d) valid
picking points, (e) wrong picking points, and (f) any picking point have been
detected.
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After several experiments in real working conditions on the
fur tanning plant, we tuned the parameters of the picking
point extraction module as follows:
(1)Blob Dimension [pixel]=300 , (2) Skeleton Dimension
[pixel]=20 , (3) Symmetrical Factor=0.5 , and (4) Branches
Dimension [pixel]=14 .
Table A summarizes the results of several tests done in order
to evaluate the ability of the vision system to successfully
detect picking points.
TABLE A
Results of several experimental tests done in order to evaluate
The ability of the vision system to successfully detect picking
Points on a hear containing a different number of furs
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Conclusion
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The performance of the system has been evaluated experimentally.
It achieves a successful picking point detection in more than 96% of
the cases while location errors are less than 4 mm. initial
implementation of the system in a fur tanning industry has retained
the success rates achieved by the subsystems while future research
is concentrated in the refinement of the manipulation strategy and
the operation speed up.
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Thanks for your attention!
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