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).
..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: [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: [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 opencv_source_code/samples/python2/mser.py
..[nister2008linear] David Nistér and Henrik Stewénius. Linear time maximally stable extremal regions. In Computer Vision–ECCV 2008, pages 183–196. Springer, 2008.
..[forssen2007maximally] Per-Erik Forssén. Maximally stable colour regions for recognition and matching. In Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on, pages 1–8. IEEE, 2007.
MSER::MSER
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The MSER constructor
..ocv:function:: MSER::MSER(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)