Inter
national
J
our
nal
of
Electrical
and
Computer
Engineering
(IJECE)
V
ol.
6,
No.
4,
August
2016,
pp.
1602
–
1609
ISSN:
2088-8708,
DOI:
10.11591/ijece.v6i4.9798
1602
I
ns
t
it
u
t
e
o
f
A
d
v
a
nce
d
Eng
ine
e
r
i
ng
a
nd
S
cie
nce
w
w
w
.
i
a
e
s
j
o
u
r
n
a
l
.
c
o
m
F
ast
Obstacle
Distance
Estimation
using
Laser
Line
Imaging
T
echnique
f
or
Smart
Wheelchair
Fitri
Utaminingrum,
Hurriyatul
Fitriyah,
Randy
Cah
ya
W
ihandika,
M
Ali
F
auzi,
Dahnial
Syauqy
,
Rizal
Maulana
F
aculty
of
Computer
Science,
Bra
wijaya
Uni
v
ersity
,
Malang,
Indonesia
Article
Inf
o
Article
history:
Recei
v
ed
Dec
25,
2015
Re
vised
May
26,
2016
Accepted
Jun
9,
2016
K
eyw
ord:
Obstacle
a
v
oidance
Distance
approximation
Laser
line
image
Blob
method
Smart
wheelchair
ABSTRA
CT
This
paper
presents
an
approach
of
obstacle
distance
estimation
for
smart
wheelchair
.
A
smart
wheelchair
w
as
equipped
with
a
camera
and
a
laser
line.
The
camera
w
as
used
to
capture
an
image
from
the
e
n
vi
ronment
in
order
to
sense
the
pathw
ay
condition.
The
laser
line
w
as
used
in
combination
with
camera
to
recognize
an
obstacle
in
the
pathw
ay
based
on
the
shape
of
laser
line
image
in
certain
angle.
A
blob
method
detection
w
as
then
applied
on
the
laser
line
image
to
separate
and
recognize
the
pattern
of
the
detected
obstacles.
The
laser
line
projector
and
camera
which
w
as
mounted
in
fix
ed-certain
position
ensured
a
fix
ed
relation
between
blobs-g
ap
and
obstacle-to-wheelchair
distance.
A
simple
linear
re
gression
from
16
obtained
data
w
as
used
to
respresent
this
relation
as
the
estimated
obstacle
distance.
As
a
result,
the
a
v
erage
error
between
the
estimation
and
the
act
ual
distance
w
as
1.25
cm
from
7
data
testing
e
xperiments.
Therefore,
the
e
xperiment
results
sho
w
that
the
proposed
method
w
as
able
to
estimate
the
distance
between
wheelchair
and
the
obstacle.
Copyright
c
2016
Institute
of
Advanced
Engineering
and
Science
.
All
rights
r
eserved.
Corresponding
A
uthor:
Fitri
Utaminingrum
F
aculty
of
Computer
Science,
Bra
wijaya
Uni
v
ersity
Malang,
Indonesia
f
3
ning
r
um
@
ub:ac:id
1.
INTR
ODUCTION
An
automatic
mo
vi
ng
object
such
as
smart
wheelchair
requires
a
se
n
s
ing
ability
to
w
ards
its
en
vironment
con-
dition.
Basicall
y
,
Smart
Wheelchair
is
a
con
v
entional
wheelchair
which
is
actuated
by
electrical
motor
and
controlled
by
a
central
processing
unit
so
that
it
can
perform
set
of
actions
based
on
the
instruction
from
the
user
.
These
user
instructions
are
generally
gi
v
en
via
jo
ysticks
or
human
v
oice
such
as
done
by
Al-Rousan
and
Assaleh
in
2011
[1].
Ho
we
v
er
,
the
user
still
plays
an
important
role
to
guide
and
monitor
the
mo
v
ement
of
the
wheelchair
especially
when
there
are
obstacles
in
the
front
or
beside
the
wheelchair
.
One
of
the
most
important
information
to
concei
v
e
is
obstruc-
tions
in
pathw
ay
which
are
b
umpiness,
hole
and
presence
of
obstacle.
The
smart
wheelchair
w
ould
ha
v
e
to
decide
an
action
once
the
poor
pathw
ay
condition
is
detected.
An
a
v
oidance
system
for
these
conditions
plays
important
role
to
secure
the
mo
ving
object
or
the
rider
.
Therefore,
in
order
to
pro
vide
more
con
v
enient
use
of
the
smart
wheelchair
,
it
is
important
to
de
v
elop
a
better
method
to
sense
and
detect
obstacles
in
the
en
vironment
around
it.
Generally
,
a
processing
of
en
vironmental
image
captured
by
a
camera
is
performed
to
sense
the
pathw
ay
condition.
In
this
paper
,
a
detection
of
poor
pathw
ay
condition
by
se
n
s
ing
the
vie
w
of
en
vironment
is
presented.
The
sight
sensing
utilizes
camera,
then
the
images
captured
were
analysed
using
image
processing
method.
Ho
we
v
er
,
by
only
utilizing
a
camera,
the
process
of
recognizing
a
poor
pathw
ay
will
need
a
longer
time
and
comple
x
computation.
Therefore,
in
order
to
simplify
image
analysis
and
reduce
algorithm
comple
xity
,
a
laser
will
be
used
in
combination
with
camera
based
image
processing
system
such
as
implemented
by
Zhang
[2]
on
weld
line
detection.
Based
on
the
inno
v
ation
and
laser
scanning
method
performed
by
T
ian
[3],
we
propose
a
ne
w
method
to
detect
and
recognize
a
poor
pathw
ay
based
on
the
shape
of
the
l
aser
line
image
shot
in
certain
angle
and
then
captured
by
a
camera.
After
obtaining
the
image,
a
blob
method
detection
w
as
performed
to
separate
and
recognize
the
pattern
o
f
the
obstacles.
Before
performing
the
obstacle
detection
by
using
laser
line
image,
it
is
preferred
to
perform
filtering
process
to
obtain
good
quality
image
source
such
as
performed
by
Utaminingrum
[4].
This
study
focuses
on
de
v
eloping
a
lo
w-computationally
poor
pathw
ays
detection
method
that
implement
the
J
ournal
Homepage:
http://iaesjournal.com/online/inde
x.php/IJECE
I
ns
t
it
u
t
e
o
f
A
d
v
a
nce
d
Eng
ine
e
r
i
ng
a
nd
S
cie
nce
w
w
w
.
i
a
e
s
j
o
u
r
n
a
l
.
c
o
m
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
2088-8708
1603
use
of
microcomputer
which
is
embedded
in
a
smart
wheelchair
system.
An
acti
v
e
imaging
method
that
utilize
a
laser
to
illuminate
the
re
gion
of
interest
is
selected
to
simplify
image
analysis
and
reduce
algorithm
comple
xity
.
In
detail,
this
paper
is
di
vided
into
fi
v
e
sections.
Section
2
pro
vides
the
o
v
ervie
w
of
related
w
orks.
In
section
3,
the
proposed
method
will
be
discussed
and
then
section
4
will
pro
vide
results
and
discussion.
Finally
,
section
5
pro
vides
conclusion
and
future
w
ork.
2.
RELA
TED
W
ORK
Man
y
approaches
ha
v
e
been
proposed
on
obstacle
detection
and
collision
a
v
oidance.
Obstacle
detection
based
on
ultrasonic
sensor
has
been
widely
used
in
autonomous
mobile
robot
application.
Ultrasonic
sensor
w
as
used
to
map
the
obstacle
by
measuring
the
distance
between
the
robot
and
obstacles.
A
simple
approach
for
detecting
obstacle
and
a
v
oiding
collision
has
been
proposed
such
by
Gageik
in
a
quadrocopter
[5]
and
o
v
erlapped
ultrasonic
sensor
method
by
Kim
[6].
Ev
en
though
ultrasonic
are
useful
in
smok
y
en
vironment,
the
e
xperiment
sho
wed
not
all
surf
aces
can
be
detected
which
mak
es
other
sensor
were
required.
Another
general
problem,
distance
estimation
using
ultrasonic
requires
additional
time
since
t
he
w
a
v
e
needs
to
tra
v
el
into
the
surf
ace
of
the
obstacle
and
return
back
to
the
recei
v
er
.
F
arther
distance
e
v
en
requires
longer
tra
v
el
time
of
the
ultrasonic
w
a
v
e.
In
2011,
Dreszer
et
al.
implemented
the
use
of
Microsoft
Kinect
sensor
to
monitor
and
learn
the
en
vironment
for
obstacle
detection
and
collision
a
v
oidance
[7].
The
Kinect
sensor
w
as
equipped
with
infrared
depth
camera
and
run
in
small
PC
which
w
as
then
mounted
on
an
in
v
erted
pendulum
robot
so
it
can
mo
v
e
and
a
v
oid
obstacles.
Similarly
,
Nissimo
v
et
al.
implemented
Kinect
sensor
in
an
agricultural
robotic
v
ehicles
to
e
xplore
greenhouse
en
vironment
[8].
The
research
pro
vided
an
approach
for
obstacle
detection
and
a
v
oidance
by
using
color
and
depth
information
obtained
from
Kine
ct
3D.
Ho
we
v
er
,
by
using
Kinect
sensor
,
shin
y
and
smooth
surf
ace
of
an
object
such
as
glass
will
pre
v
ent
infrared
w
a
v
e
to
be
reflected
back
which
cause
some
error
.
Barreto
et
al.
proposed
a
method
to
measure
a
distance
based
on
laser
distance
triangulation
for
image
processing
[9].
He
de
v
eloped
a
measurement
technique
on
an
embedded
system
which
required
smaller
processing
po
wer
compared
to
personal
computer
as
x86
architecture.
He
use
d
a
CMOS
camera
and
a
laser
to
implement
the
laser
triangul
ation
distance
measurement
.
Ho
we
v
er
,
the
system
w
as
only
aimed
to
det
ect
the
distance.
F
or
a
mo
ving
wheelchair
,
instead
of
only
considering
the
distance,
it
may
also
g
ain
benefit
if
the
system
can
recognize
the
height
of
an
obstacle
to
decide
the
ne
xt
action:
whether
to
completely
a
v
oid
or
to
dri
v
e
abo
v
e
it.
3.
PR
OPOSED
METHOD
This
study
aims
to
detect
an
obstacle
based
on
the
response
gi
v
en
by
the
laser
line
projection
on
the
pathw
ay
.
First,
a
line
of
laser
w
as
projected
onto
the
pathw
ay
and
a
colored
image
of
its
vie
w
w
as
acquired
using
a
fix
ed
CCD
(Char
ge-Coupled
De
vice)
camera.
A
laser
light
w
as
chosen
for
its
focus
and
high
intensity
characteristic.
The
laser
line
and
the
camera
were
mounted
in
specific
angle
triangular
configuration
which
will
be
discuss
ed
more
in
the
ne
xt
subsection.
The
captured
image
w
as
then
analyzed
through
image
processing
methods
which
consist
of
color
-space
con
v
ersion,
se
gmentation,
closing
morphol
og
i
cal
filtering
and
blob
detection
(Figure
1).
The
number
of
detected
blobs
and
their
centroids
w
ould
be
used
as
features
in
the
obstacle
distance
calculation.
The
main
processing
unit
used
to
process
the
image
w
as
Raspberry
Pi
2
with
900
MHz
quadcore
ARM
Corte
x-A7
CPU
and
1
GB
of
RAM
equipped
with
standard
Raspbian
Wheezy
OS
and
OpenCV
v
ersion
3.0.
3.1.
Image
acquisition
of
P
athways
view
The
laser
line
used
in
this
study
w
as
LN60-650
(class
2)
that
has
650
nm
w
a
v
elength
and
60
f
an
angle.
It
projected
a
focused
red
line
onto
the
pathw
ay
.
The
laser
line
projector
w
as
mounted
in
v
ertical
pole
in
fix
ed
height
of
1.4
metres
abo
v
e
the
floor
and
at
fix
ed
angle
of
60
resulting
a
projection
at
2.44
meters
a
w
ay
horizontally
from
the
laser
mounted
on
the
wheelchair
.
A
CCD
camera
w
as
used
to
acquire
the
images
that
result
RGB
color
images
each
of
320x240
pix
el
size.
This
camera
w
as
also
mounted
in
a
horizontal
pole
with
fix
ed
0.75
meters
height
abo
v
e
the
floor
.
Since
the
laser
and
camera
were
placed
in
a
fix
ed
position,
the
projected
laser
w
as
ensured
to
be
in
a
fix
ed
location
in
the
captured
image
as
well.
The
triangular
configurations
of
laser
line
image
and
CCD
camera
are
sho
wn
in
Figure
2
while
the
acquired
image
illustration
i
s
sho
wn
in
Figure
3.
These
configurations
were
arranged
to
assure
the
wheel-chair
w
ould
response
to
obstacle
in
1
meter
beforehand
in
order
to
secure
the
rider
from
stumbling
o
v
er
or
crashing
into
the
obstacle.
F
ast
Obstacle
Distance
Estimation
using
Laser
Line
Ima
ging
T
ec
hnique
for
Smart
Wheelc
hair
(F
Utaminingrum)
Evaluation Warning : The document was created with Spire.PDF for Python.
1604
ISSN:
2088-8708
Figure
1.
Feature
e
xtraction
of
obstacle
detection
using
image
processing.
(a)
Mounting
(b)
Angle
configuration
Figure
2.
Laser
line
and
camera
mounted
on
the
wheelchair
Figure
3.
Captured
image
illustration.
IJECE
V
ol.
6,
No.
4,
August
2016:
1602
–
1609
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
2088-8708
1605
3.2.
Color
-Space
Con
v
ersion
fr
om
RGB
to
HSV
The
acquired
image
in
RGB
color
-space
w
as
then
con
v
erted
into
HSV
color
-space.
HSV
color
-space
which
comprises
Hue,
Saturation
and
V
alue
is
mainly
used
in
se
gmentation
where
the
object
of
interest
is
a
color
-specific.
The
study
done
by
Mesk
o
in
2013
and
by
Chmelar
in
2015
used
this
color
-space
to
detect
red
laser
projection
[11,
10].
3.3.
Laser
-Line
Segmentation
using
Thr
esholding
Method
Mesk
o
and
Chmelar
used
laser
line
that
has
characteristics
of
red
color
and
high
intensity
.
In
this
study
,
the
red
color
has
Hue
of
(0,
70,
70)
to
(255,
255,
255)
in
0
-
255
intensity
le
v
el.
This
range
w
as
then
used
as
threshold
to
se
gment
the
red
laserline
from
the
background.
Pix
els
in
the
cropped
images
whose
intensity
f
all
within
both
thresholds
w
as
then
mask
ed
as
1,
otherwise
it
w
as
a
non-laser
object
and
mask
ed
as
0.
3.4.
Mor
phological
Filtering
Closing
is
a
type
of
morphological
filter
for
binary
image
that
uses
a
structuring
element
to
produce
refined
binary
image
within
bounding
box
on
the
se
gmented
image.
The
closing
used
dilation
process
follo
wed
by
erosion.
In
this
study
,
a
2x2
square
structuring
elements
is
used
for
the
closing
process.
If
there
is
a
v
alue
of
1
in
the
se
gmented
image,
then
dilation
process
will
gi
v
e
v
alue
of
1
into
its
surrounding
2x2
pix
els.
In
the
other
hand,
erosion
process
change
the
v
alue
of
1
in
the
se
gmented
image
into
0
i
f
the
2x2
surrounding
pix
els
did
not
follo
w
the
structuring
element.
Both
process
w
ould
produce
an
enclosed
se
gmented
laser
line.
The
closing
process
is
then
follo
wed
by
another
dilation
process
using
a
horizontal-shape
structuring
element
of
3x15
in
order
to
connect
the
line
discontinuities.
3.5.
Blob
Analysis
Figure
4.
Captured
laser
line
image
and
the
labeled-blob
Image.
A
blob
in
binary
image
is
a
re
gion
of
adjacent
connected-component.
This
method
w
as
performed
after
se
g-
mentation
to
label
each
contiguous
fore
ground
pix
els.
Labelling
is
a
process
of
scanning
fore
ground
pix
els
(intensity
of
1)
from
top-right
to
the
top-left
and
mo
v
e
to
subsequent
lo
wer
pix
els
[12]
and
label
each
fore
ground
pix
els
found
in
ascending
order
.
This
algorithm
processes
e
v
ery
ro
w
at
a
time.
The
first
fore
ground
pix
el
found
is
labeled
as
1
and
the
second
as
2
and
so
forth.
This
study
used
an
8-connected
fore
ground
which
means
consecuti
v
e
fore
ground
pix
els
w
as
assigned
to
its
north,
east,
south,
west,
north-east,
south-east,
north-west
and
south-west
neighbor’
s
label.
Each
label
w
as
then
assigned
as
a
blob
.
Figure
4
sho
ws
the
result
of
blob
detection
on
the
laser
line
image.
There
are
three
detected
blobs
which
were
illustrated
as
circles
on
the
laser
line
image
since
the
line
w
as
split
into
three
parts.
More
obstacles
generated
more
blobs
such
as
sho
wn
in
Figure
5.
Therefore,
it
is
still
possible
to
detect
small
and
thin
objects
such
as
the
foot
of
a
chair
and
table.
The
centroids
were
then
calculated
using
the
moment
of
the
image
or
the
center
of
mass
such
as
illustrated
in
Equation
1
and
2.
center
x
is
centroid
coordinate
in
horizontal
axis,
while
center
y
is
centroid
coordinate
in
v
ertica
l
axis.
n
represents
number
of
pix
els
belonging
to
the
se
gmented
blob
.
x
k
and
y
k
are
the
coordinates
of
blobs
in
horizontal
and
v
ertical
axis
respecti
v
ely
.
The
number
of
label
and
all
centroids
w
ould
be
used
in
obstacle
detection
and
obstacle-to-wheelchair
distance
estimation.
F
ast
Obstacle
Distance
Estimation
using
Laser
Line
Ima
ging
T
ec
hnique
for
Smart
Wheelc
hair
(F
Utaminingrum)
Evaluation Warning : The document was created with Spire.PDF for Python.
1606
ISSN:
2088-8708
Figure
5.
Captured
laser
line
image
and
the
labeled-blob
Image.
center
x
=
1
n
n
X
k
=1
x
k
(1)
center
y
=
1
m
m
X
k
=1
y
k
(2)
3.6.
Obstacle-Distance
Estimation
using
Linear
Regr
ession
A
no
obstacle
condition
w
as
defined
where
only
1
blob
w
as
detected
in
the
labeled-blob
image,
meaning
that
there
w
as
no
object
destructs
the
laser
projection.
Once
an
obstacle
presents
in
the
pathw
ay
,
the
number
of
connected-components
label
becomes
tw
o
or
three
where
one
higher
blob
appears
on
the
obstacle.
T
w
o
labels
mean
the
obstacle
presents
on
the
side
of
pathw
ay
while
three
labels
mean
the
obstacle
presents
in
the
middle
of
pathw
ay
.
The
centroid
coordinate
of
higher
blob
(
center
H
x
;
center
H
y
)
w
as
then
subtracted
with
the
centroid
of
lo
wer
blob
(
center
L
x
;
center
L
y
)
resulting
a
blobs-g
ap.
The
laser
projector
and
camera
that
w
as
mounted
in
fix
ed
position
ensured
a
fix
ed
relation
between
blobs-
g
ap
and
obstacle-to-wheelchair
distance.
A
simple
linear
re
gression
w
as
used
to
respresent
this
relation.
Gi
v
en
there
are
n
number
of
collected
blobs-g
aps
(
x
i
),
y
i
correspond
to
obstacle-to-wheelchair
distances,
x
and
y
are
respecti
v
e
a
v
erages,
the
coef
ficient
a
and
constant
b
of
linear
re
gression
y
=
ax
+
b
were
calculated
using
Equation
3
and
4.
a
=
P
n
i
=1
(
x
i
(
x
))(
y
i
(
y
))
P
n
i
=1
(
x
i
(
x
))
2
(3)
b
=
y
a
x
(4)
In
addition
of
measuring
the
distance,
the
triangular
configuration
of
the
laser
line
and
the
camera
also
pro
vides
indirect
information
about
the
height
of
the
obstacle
based
on
assumption.
When
there
are
more
than
1
detected
blob,
it
means
that
there
is
an
obstacle
obstructing
the
laser
line
projection.
If
the
wheelchair
mo
v
es
forw
ard,
the
pix
el
distance
of
detected
bl
ob
will
increase
and
it
will
be
used
to
calculate
the
distance.
Ho
we
v
er
,
if
after
specific
time
the
blob
number
get
lo
wered,
then
it
can
be
concluded
that
the
height
of
the
obstacle
is
lo
w
,
and
the
wheelchair
can
continue
to
mo
v
e
forw
ard
and
pass
o
v
er
the
obstacle.
Therefore,
the
hei
ght
assumption
of
the
obstacle
can
be
used
to
decide
what
action
should
be
tak
en
by
the
wheelchair;
whether
it
should
stop
or
pass
o
v
er
the
obstacle.
4.
RESUL
T
AND
DISCUSSION
Re
gression
analysis
of
the
relation
between
blobs
-g
a
p
and
the
actual
obstacle-to-wheel
chair
distance
is
sho
wn
in
Figure
6.
In
order
to
obtain
the
re
gression
formula,
16
data
with
dif
ferent
actual
obstacle
distance
were
captured
using
laser
line
imaging
technique.
There
are
tw
o
types
of
dat
a,
the
triangle
dots
sho
w
the
data
for
obstacle
with
20
cm
width
while
the
circle
dots
sho
w
the
data
for
obstacle
with
33
cm
width.
IJECE
V
ol.
6,
No.
4,
August
2016:
1602
–
1609
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
2088-8708
1607
From
the
data
sho
wn
in
the
Figure,
the
re
gression
formula
can
be
obtained
from
the
number
of
pix
el
and
actual
distance
by
using
Equation
3
and
4:
y
=
1
:
06
x
+
155
:
73
(5)
Figure
6.
Re
gression
Analysis
of
the
Blob
Distance
in
Pix
els
and
the
Actual
Distance.
Where
x
is
the
number
of
pix
el,
and
y
is
the
distance
between
the
wheelchair
and
the
obstacles.
Finally
,
Equation
5
w
as
then
used
to
estimate
actual
distance
of
7
testing
data.
The
results
of
these
calculation
are
sho
wn
in
T
able
1.
From
the
T
able,
it
can
be
concluded
that
the
a
v
erage
error
of
measurements
is
1.25
cm.
The
w
orst
distance
estimation
happened
when
the
actual
distance
w
as
80
cm,
b
ut
w
as
estimated
as
74.19
cm.
it
w
as
probably
caused
by
bad
data
on
the
training
phase
whi
ch
lead
to
less
accurate
re
gression
formula.
The
best
di
stance
estimation
happened
when
the
actual
distance
w
as
90
cm
where
it
only
g
a
v
e
error
of
0.06
cm.
The
captured
line
laser
image
had
320x240
pix
els
which
w
as
completely
computed
in
83
ms.
T
able
1.
Experiment
Results
Using
Re
gression
F
ormula.
Actual
(cm)
Blob
Pixels
(pixels)
Estimated
(cm)
Err
or
(cm)
80
76.92
74.19
5.81
90
62.07
89.94
0.06
100
52.94
99.61
0.39
110
43.31
109.82
0.18
120
33.01
120.74
0.74
130
24.68
129.57
0.43
140
15.93
138.84
1.16
A
v
erage
1.25
5.
CONCLUSION
In
this
paper
,
a
method
to
estimate
the
distance
between
wheelchair
and
obstacle
has
been
presented.
By
using
laser
line
along
with
camera
which
w
as
mounted
in
fix
ed-certain
position,
a
linear
re
gression
can
be
performed
to
relate
the
number
of
pix
els
in
the
captured
image
with
the
actual
distance
between
the
wheelchair
and
obstacles.
A
F
ast
Obstacle
Distance
Estimation
using
Laser
Line
Ima
ging
T
ec
hnique
for
Smart
Wheelc
hair
(F
Utaminingrum)
Evaluation Warning : The document was created with Spire.PDF for Python.
1608
ISSN:
2088-8708
simple
linear
re
gression
from
16
obtained
data
w
as
used
to
represent
this
relation
as
the
estimated
obstacle
distance.
As
a
result,
the
a
v
erage
error
between
the
estimation
and
the
actual
distance
w
as
1.25
cm
from
7
data
testing
e
xperiments.
From
this
result,
it
can
be
seen
that
this
method
is
able
to
estimate
the
distance
between
wheelchair
and
the
obstacles.
Ho
we
v
er
,
there
were
still
dra
wbacks
in
this
research
since
it
hea
vily
depends
on
the
laser
line
image.
One
of
the
problem
happened
when
the
light
captured
by
the
camera
w
as
too
bright
which
mak
e
it
hard
to
obtain
clear
laser
line
image.
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BIOGRAPHIES
OF
A
UTHORS
Fitri
Utaminingrum
w
as
born
in
Surabaya,
East
Ja
v
a,
Indonesia.
She
recei
v
ed
her
Bachelor
de-
gree
in
Electrical
Engineering
(BEng.)
from
National
Institute
of
T
echnology
(2000-2004),
and
master
de
gree
in
the
same
major
(MEng.)
from
Bra
wijaya
Uni
v
ersity
Malang,
Indonesia
in
2007.
In
addition,
she
obtained
the
de
gree
of
Doctor
of
Engineering
in
the
field
of
Computer
Science
and
Electrical
Engineering
from
K
umamoto
Uni
v
ersity
,
Japan
(2011-2014).
She
al
so
has
successfully
completed
International
Joint
Education
Program
from
Science
and
technology
at
Graduate
School
of
Science
and
T
echnology
,
K
umamoto
Uni
v
ersity
,
Japan.
She
has
been
w
orking
as
part
time
lec-
turer
in
se
v
eral
institution,
such
as
Sek
olah
T
inggi
T
eknik
Angkatan
darat
(STT
AD)
from
2006
until
2007.
Sek
olah
T
inggi
T
eknik
Atlas
Nusantara
(STT
AR)
start
from
2006
until
2007.
In
addition,
she
also
has
been
teaching
the
students
of
V
ocational
Education
De
v
elopment
Center
(VEDC)
Malang-
Indonesia
at
2007
and
Malang
Joint
Campus
(MJC)
at
2007.
She
has
been
full
time
l
ecturer
in
Bra
wijaya
Uni
v
ersity
start
from
2008.
IJECE
V
ol.
6,
No.
4,
August
2016:
1602
–
1609
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
2088-8708
1609
Hurriyatul
Fitriyah
is
a
lecturer
in
F
aculty
of
Computer
Science,
Uni
v
ersity
of
Bra
wijaya.
She
recei
v
ed
the
bachelor
de
gree
in
Ph
ysics
Engineering
from
Institut
T
eknologi
Sepuluh
Nopember
(ITS),
Surabaya,
Indonesia
in
2007
and
the
Master
De
gree
in
Electrical
and
Electronic
Engineering
from
Uni
v
ersiti
T
eknologi
Petronas
(UTP),
Perak,
Malaysia
in
2012.
Her
research
interest
includes
computer
vision
and
pattern
recognition.
She
is
one
of
the
author
in
”Surf
ace
Imaging
for
Biomedi-
cal
Application”
published
by
CRC
Press
in
2014
and
the
co-in
v
entor
in
patent
of
”A
methodology
and
Apparatus
for
Objecti
v
e
Assessment
and
Rating
of
Psoriasis
Lesion
Thickness
using
Digital
Imaging”
in
Malaysia,
German
and
US.
Randy
Cah
ya
W
ihandika
,
male,
recei
v
ed
the
bac
helor
de
gree
from
Electronic
Engineering
Poly-
technic
Institute
of
Surabaya,
Indonesia,
in
2011
and
master
de
gree
at
Department
of
Informatics,
Institut
T
eknologi
Sepuluh
Nopember
,
Surabaya,
Indonesia
in
2013.
His
research
interests
include
computer
vision,
digital
image
processing,
and
pattern
recognition.
M.
A
li
F
auzi
is
curre
ntly
w
orking
at
Intelligent
System
Laboratory
,
Bra
wijaya
Uni
v
ersity
.
H
e
ob-
tained
his
Bachelor
De
gree
in
Inf
ormatics
from
Institut
T
eknologi
Sepuluh
Nopember
(Indonesia)
in
2011.
His
Ma
ster
De
gree
in
Informatics
obtained
from
Institut
T
eknologi
Sepuluh
Nopember
(Indonesia)
in
2013.
His
researches
are
in
fields
of
Intelligent
Syste
m,
and
Natural
Language
Pro-
cessing.
Dahnial
Syauqy
recei
v
ed
Bachelor
De
gree
in
Electrical
Engineering
from
Bra
wijaya
Uni
v
ersity
(Indonesia)
in
2009.
He
recei
v
ed
his
Master
De
gree
in
Electrical
Engineering
from
National
Cen-
tral
Uni
v
ersity
(T
aiw
an)
in
2014.
He
is
currently
w
orking
at
Laboratory
of
Computer
System
and
Robotics
in
Bra
wijaya
Uni
v
ersity
.
His
current
research
interests
focus
in
the
areas
of
electronics,
embedded
system
and
signal
processing.
Rizal
Maulana
is
currently
w
orking
at
Laboratory
of
Computer
System
and
Robotics
in
Bra
wijaya
Uni
v
ersity
.
He
obtained
Bachelor
De
gree
in
Elec
trical
Engineering
from
Bra
wijaya
Uni
v
ersity
(Indonesia)
in
2011.
His
Master
De
gree
in
Electrical
Engineering
obtained
from
National
Central
Uni
v
ersity
(T
aiw
an)
in
2014.
His
researches
are
in
fields
of
electronic
s,
robotics
and
biomedical
signal
processing.
F
ast
Obstacle
Distance
Estimation
using
Laser
Line
Ima
ging
T
ec
hnique
for
Smart
Wheelc
hair
(F
Utaminingrum)
Evaluation Warning : The document was created with Spire.PDF for Python.