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b3fb4986
编写于
9月 30, 2011
作者:
M
Maria Dimashova
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restored doc on latent svm that was lost in moving to rst
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modules/objdetect/doc/latent_svm.rst
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modules/objdetect/doc/latent_svm.rst
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Latent
SVM
===============================================================
..
highlight
::
cpp
Discriminatively
Trained
Part
Based
Models
for
Object
Detection
---------------------------------------------------------------
The
object
detector
described
below
has
been
initially
proposed
by
P
.
F
.
Felzenszwalb
in
[
Felzenszwalb2010
]
_
.
It
is
based
on
a
Dalal
-
Triggs
detector
that
uses
a
single
filter
on
histogram
of
oriented
gradients
(
HOG
)
features
to
represent
an
object
category
.
This
detector
uses
a
sliding
window
approach
,
where
a
filter
is
applied
at
all
positions
and
scales
of
an
image
.
The
first
innovation
is
enriching
the
Dalal
-
Triggs
model
using
a
star
-
structured
part
-
based
model
defined
by
a
"root"
filter
(
analogous
to
the
Dalal
-
Triggs
filter
)
plus
a
set
of
parts
filters
and
associated
deformation
models
.
The
score
of
one
of
star
models
at
a
particular
position
and
scale
within
an
image
is
the
score
of
the
root
filter
at
the
given
location
plus
the
sum
over
parts
of
the
maximum
,
over
placements
of
that
part
,
of
the
part
filter
score
on
its
location
minus
a
deformation
cost
easuring
the
deviation
of
the
part
from
its
ideal
location
relative
to
the
root
.
Both
root
and
part
filter
scores
are
defined
by
the
dot
product
between
a
filter
(
a
set
of
weights
)
and
a
subwindow
of
a
feature
pyramid
computed
from
the
input
image
.
Another
improvement
is
a
representation
of
the
class
of
models
by
a
mixture
of
star
models
.
The
score
of
a
mixture
model
at
a
particular
position
and
scale
is
the
maximum
over
components
,
of
the
score
of
that
component
model
at
the
given
location
.
CvLSVMFilterPosition
--------------------
..
ocv
:
struct
::
CvLSVMFilterPosition
Structure
describes
the
position
of
the
filter
in
the
feature
pyramid
.
..
ocv
:
member
::
unsigned
int
l
level
in
the
feature
pyramid
..
ocv
:
member
::
unsigned
int
x
x
-
coordinate
in
level
l
..
ocv
:
member
::
unsigned
int
y
y
-
coordinate
in
level
l
CvLSVMFilterObject
------------------
..
ocv
:
struct
::
CvLSVMFilterObject
Description
of
the
filter
,
which
corresponds
to
the
part
of
the
object
.
..
ocv
:
member
::
CvLSVMFilterPosition
V
ideal
(
penalty
=
0
)
position
of
the
partial
filter
from
the
root
filter
position
(
V_i
in
the
paper
)
..
ocv
:
member
::
float
fineFunction
[
4
]
vector
describes
penalty
function
(
d_i
in
the
paper
)
pf
[
0
]
*
x
+
pf
[
1
]
*
y
+
pf
[
2
]
*
x
^
2
+
pf
[
3
]
*
y
^
2
..
ocv
:
member
::
int
sizeX
,
sizeY
Rectangular
map
(
sizeX
x
sizeY
),
every
cell
stores
feature
vector
(
dimension
=
p
)
..
ocv
:
member
::
int
numFeatures
number
of
features
..
ocv
:
member
::
float
*
H
matrix
of
feature
vectors
to
set
and
get
feature
vectors
(
i
,
j
)
used
formula
H
[(
j
*
sizeX
+
i
)
*
p
+
k
],
where
k
-
component
of
feature
vector
in
cell
(
i
,
j
)
CvLatentSvmDetector
-------------------
..
ocv
:
struct
::
CvLatentSvmDetector
Structure
contains
internal
representation
of
trained
Latent
SVM
detector
.
..
ocv
:
member
::
int
num_filters
total
number
of
filters
(
root
plus
part
)
in
model
..
ocv
:
member
::
int
num_components
number
of
components
in
model
..
ocv
:
member
::
int
*
num_part_filters
array
containing
number
of
part
filters
for
each
component
..
ocv
:
member
::
CvLSVMFilterObject
**
filters
root
and
part
filters
for
all
model
components
..
ocv
:
member
::
float
*
b
biases
for
all
model
components
..
ocv
:
member
::
float
score_threshold
confidence
level
threshold
CvObjectDetection
-----------------
..
ocv
:
struct
::
CvObjectDetection
Structure
contains
the
bounding
box
and
confidence
level
for
detected
object
.
..
ocv
:
member
::
CvRect
rect
bounding
box
for
a
detected
object
..
ocv
:
member
::
float
score
confidence
level
cvLoadLatentSvmDetector
-----------------------
Loads
trained
detector
from
a
file
.
..
ocv
:
function
::
CvLatentSvmDetector
*
cvLoadLatentSvmDetector
(
const
char
*
filename
)
:
param
filename
:
Name
of
the
file
containing
the
description
of
a
trained
detector
cvReleaseLatentSvmDetector
--------------------------
Release
memory
allocated
for
CvLatentSvmDetector
structure
.
..
ocv
:
function
::
void
cvReleaseLatentSvmDetector
(
CvLatentSvmDetector
**
detector
)
:
param
detector
:
CvLatentSvmDetector
structure
to
be
released
cvLatentSvmDetectObjects
------------------------
Find
rectangular
regions
in
the
given
image
that
are
likely
to
contain
objects
and
corresponding
confidence
levels
.
..
ocv
:
function
::
CvSeq
*
cvLatentSvmDetectObjects
(
IplImage
*
image
,
CvLatentSvmDetector
*
detector
,
CvMemStorage
*
storage
,
float
overlap_threshold
,
int
numThreads
)
:
param
image
:
image
:
param
detector
:
LatentSVM
detector
in
internal
representation
:
param
storage
:
Memory
storage
to
store
the
resultant
sequence
of
the
object
candidate
rectangles
:
param
overlap_threshold
:
Threshold
for
the
non
-
maximum
suppression
algorithm
:
param
numThreads
:
Number
of
threads
used
in
parallel
version
of
the
algorithm
..
[
Felzenszwalb2010
]
Felzenszwalb
,
P
.
F
.
and
Girshick
,
R
.
B
.
and
McAllester
,
D
.
and
Ramanan
,
D
.
*
Object
Detection
with
Discriminatively
Trained
Part
Based
Models
*.
PAMI
,
vol
.
32
,
no
.
9
,
pp
.
1627
-
1645
,
September
2010
modules/objdetect/doc/objdetect.rst
浏览文件 @
b3fb4986
...
...
@@ -8,3 +8,4 @@ objdetect. Object Detection
:maxdepth: 2
cascade_classification
latent_svm
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