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体验新版 GitCode,发现更多精彩内容 >>
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8524d46f
编写于
11月 30, 2015
作者:
A
Alexander Alekhin
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Merge pull request #5633 from mshabunin:doc-mser
上级
e860ed76
7392ce0a
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2
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48 addition
and
9 deletion
+48
-9
doc/opencv.bib
doc/opencv.bib
+16
-0
modules/features2d/include/opencv2/features2d.hpp
modules/features2d/include/opencv2/features2d.hpp
+32
-9
未找到文件。
doc/opencv.bib
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8524d46f
...
...
@@ -848,3 +848,19 @@
year={2007},
publisher={Springer}
}
@incollection
{nister2008linear,
title={Linear time maximally stable extremal regions},
author={Nist{\'e}r, David and Stew{\'e}nius, Henrik},
booktitle={Computer Vision--ECCV 2008},
pages={183--196},
year={2008},
publisher={Springer}
}
@inproceedings
{forssen2007maximally,
title={Maximally stable colour regions for recognition and matching},
author={Forss{\'e}n, Per-Erik},
booktitle={Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on},
pages={1--8},
year={2007},
organization={IEEE}
}
modules/features2d/include/opencv2/features2d.hpp
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8524d46f
...
...
@@ -317,25 +317,48 @@ public:
CV_WRAP
virtual
int
getFastThreshold
()
const
=
0
;
};
/** @brief Maximally stable extremal region extractor
. :
/** @brief Maximally stable extremal region extractor
The class encapsulates all the parameters of the MSER extraction algorithm (see
<http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions>). Also see
<http://code.opencv.org/projects/opencv/wiki/MSER> for useful comments and parameters description.
The class encapsulates all the parameters of the %MSER extraction algorithm (see [wiki
article](http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions)).
@note
- (Python) A complete example showing the use of the MSER detector can be found at
opencv_source_code/samples/python2/mser.py
*/
- there are two different implementation of %MSER: one for grey image, one for color image
- the grey image algorithm is taken from: @cite nister2008linear ; the paper claims to be faster
than union-find method; it actually get 1.5~2m/s on my centrino L7200 1.2GHz laptop.
- the color image algorithm is taken from: @cite forssen2007maximally ; it should be much slower
than grey image method ( 3~4 times ); the chi_table.h file is taken directly from paper's source
code which is distributed under GPL.
- (Python) A complete example showing the use of the %MSER detector can be found at samples/python2/mser.py
*/
class
CV_EXPORTS_W
MSER
:
public
Feature2D
{
public:
//! the full constructor
/** @brief Full consturctor for %MSER detector
@param _delta it compares \f$(size_{i}-size_{i-delta})/size_{i-delta}\f$
@param _min_area prune the area which smaller than minArea
@param _max_area prune the area which bigger than maxArea
@param _max_variation prune the area have simliar size to its children
@param _min_diversity for color image, trace back to cut off mser with diversity less than min_diversity
@param _max_evolution for color image, the evolution steps
@param _area_threshold for color image, the area threshold to cause re-initialize
@param _min_margin for color image, ignore too small margin
@param _edge_blur_size for color image, the aperture size for edge blur
*/
CV_WRAP
static
Ptr
<
MSER
>
create
(
int
_delta
=
5
,
int
_min_area
=
60
,
int
_max_area
=
14400
,
double
_max_variation
=
0.25
,
double
_min_diversity
=
.2
,
int
_max_evolution
=
200
,
double
_area_threshold
=
1.01
,
double
_min_margin
=
0.003
,
int
_edge_blur_size
=
5
);
/** @brief Detect %MSER regions
@param image input image (8UC1, 8UC3 or 8UC4)
@param msers resulting list of point sets
@param bboxes resulting bounding boxes
*/
CV_WRAP
virtual
void
detectRegions
(
InputArray
image
,
CV_OUT
std
::
vector
<
std
::
vector
<
Point
>
>&
msers
,
std
::
vector
<
Rect
>&
bboxes
)
=
0
;
...
...
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