2EEE
TRANSACTIONS
ON
SYSTREMS,
MAN,
AND
CYBERNETICS,
VOL.
SMC-9,
NO.
1,
JANUARY
1979
7*
LONGITUDE
Fig.
6.
ID
plot
of ship
10001
after
the
second
round
of
operator-imposed
assign-
ment
constraints.
LONGITUDE
Fig.
7.
Actual
ship
movements.
of
the
two
last
sighted
locations.
The
true
trajectories
are
shown
in
Fig.
7
where
it
can
be
seen
that
ship
10001
did,
in
fact,
turn
toward
the
coast.
IV.
CONCLUDING
REMARKS
The
procedure
of
ship
identification
from
DF
sightings
has
been
oversimplified
in
this
discussion.
Often
DF
sightings
are
not
completely
identified
but,
instead,
contain
only
ship
class
informa-
tion.
The
interactive
technique
still
applies,
but
additional
identification
and
display
flexibility
must
be
provided.
Any
additional
information
contained
in
the
sightings
can
be
used
to
discriminate
among
radar
and
DF
sightings.
Factors
such
as
measured
heading
and
visual
ID
will
permit
further
automatic
reduction
of
the
P
and
Q
matrices.
It
is
also
possible
to
automate
some
of
the
more
routine
manual
functions.
However,
experience
has
shown
that
better
results
are
obtained
by
having
a
human
operator
resolve
ambiguous
situa-
tions
arising
from
sparse
data.
REFERENCES
[I]
R.
W.
Sittler,
"An
optimal data
association
problem
in
surveillance
theory,"
IEEE
Trans.
Mil.
Elect.,
vol.
MIL-8,
pp.
125-139,
1964.
[2]
M.
S.
White,
"Finding
events
in
a
sea
of
bubbles,"
IEEE
Trans.
Comput.,
voL
C-20
(9)
pp.
988-1006,
1971.
[3}
A.
G.
Jaffer
and
Y.
Bar-Shalom.
"On
optimal
tracking
in
multiple-target
environ-
ments,"
Proc.
of
the
3rd
Sym.
on
Nonlinear
Estimation
Theory
and
its
Applications,
San
Diego,
CA,
Sept.
1972.
[4]
P.
Smith
and
G.
Buechler,
"A
branching
algorithm
for
discrimination
and
track-
ing
multiple
objects,"
IEEE
Trans.
Automat.
Contr.,
vol.
AC-20,
pp.
101-104,
1975.
[5]
D.
L.
Alspach,
'A
Gaussian
sum
approach
to
the
multitarget-tracking
problem,"
Automatica,
vol.
11,
pp.
285-296,1975.
[6]
C.
L.
Morefield,
Application
of
0-1
Integer
Programming
to
a
Track
Assembly
Problem,
TR-0075(5085-10II,
Aerospace
Corp.
El
Segundo,
CA,
Apr.
1975.
[7]
D.
B.
Reid,
A
Multiple
Hypothesis
Filter
for
Tracking
Multiple
Targets
in
a
Cluttered
Environment,
LMSC-D560254,
Lockheed
Palo
Alto
Research
Labora-
tories,
Palo
Alto,
CA,
Sept.
1977.
[8]
P.
L.
Smith,
"Reduction
of
sea
surveillance
data
using
binary
matrices,"
IEEE
Trans.
Syst.,
Man,
Cybern.,
vol.
SMC-6
(8),
pp.
531-538,
Aug.
1976.
A
Tlreshold
Selection
Method
from
Gray-Level
Histograms
NOBUYUKI
OTSU
Abstract-A
nonparametric
and
unsupervised
method
of
automa-
tic
threshold
selection
for
picture
segmentation
is
presented.
An
optimal
threshold
is
selected
by
the
discriminant
criterion,
namely,
so
as
to
maximize
the
separability
of
the
resultant
classes
in
gray
levels.
The
procedure
is
very
simple,
utilizing
only
the
zeroth-
and
the
first-order
cumulative
moments
of
the
gray-level
histogram.
It
is
straightforward
to
extend
the
method
to
multithreshold
problems.
Several
experimental
results
are
also
presented
to
support
the
validity
of
the
method.
I.
INTRODUCTION
It
is
important
in
picture
processing
to
select
an
adequate
thre-
shold
of
gray
level
for
extracting
objects
from
their
background.
A
variety
of
techniques
have
been
proposed
in
this
regard.
In
an
ideal
case,
the
histogram
has
a
deep
and
sharp
valley
between
two
peaks
representing
objects
and
background,
respectively,
so
that
the
threshold
can
be
chosen
at
the
bottom
of
this
valley
[1].
However,
for
most
real
pictures,
it
is
often
difficult
to
detect
the
valley
bottom
precisely,
especially
in
such
cases
as
when
the
valley
is
flat
and
broad,
imbued
with
noise,
or
when
the
two
peaks
are
extremely
unequal
in
height,
often
producing
no
traceable
valley.
There
have
been
some
techniques
proposed
in
order
to
overcome
these
difficulties.
They
are,
for
example,
the
valley
sharpening
technique
[2],
which
restricts
the
histogram
to
the
pixels
with
large
absolute
values
of
derivative
(Laplacian
or
gradient),
and
the
difference
histogram
method
[3],
which
selects
the
threshold
at
the
gray
level
with
the
maximal
amount
of
difference.
These
utilize
information
concerning
neighboring
pixels
(or
edges)
in
the
ori-
ginal
picture
to
modify
the
histogram
so
as
to
make
it
useful
for
thresholding.
Another
class
of
methods
deals
directly
with
the
gray-level
histogram
by
parametric
techniques.
For
example,
the
histogram
is
approximated
in
the
least
square
sense
by
a
sum
of
Gaussian
distributions,
and
statistical
decision
procedures
are
applied
[4].
However,
such
a
method
requires
considerably
ted-
ious
and
sometimes
unstable
calculations.
Moreover,
in
many
cases,
the
Gaussian
distributions
turn
out
to
be
a
meager
approxi-
mation
of
the
real
modes.
In
any
event,
no
"goodness"
of
threshold
has
been
evaluated
in
Manuscript
received
October
13,
1977;revised
April
17,1978
and
August
31,
1978.
The
author
is
with
the
Mathematical
Engineering
Section,
Information
Science
Division,
Electrotechnical
Laboratory,
Chiyoda-ku,
Tokyo
100,
Japan.
0018-9472/79/0100-0062$00.75
(D
1979
IEEE
62
t
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blue_olivine2011-12-13论文是英文的，可以看一下
