1.MSER
MSER最大穩(wěn)定極值區(qū)域（MSER-Maximally Stable Extremal Regions），該算法是2002提出的，主要是基于分水嶺的思想來做圖像中斑點(diǎn)的檢測。
原理：MSER對一幅已經(jīng)處理成灰度的圖像做二值化處理，這個(gè)處理的閾值從0到255遞增，這個(gè)閾值的遞增類似于在一片土地上做水平面的上升，隨著水平面上升，高高低低凹凸不平的土地區(qū)域就會不斷被淹沒，這就是分水嶺算法，而這個(gè)高低不同，就是圖像中灰度值的不同。而在一幅含有文字的圖像上，有些區(qū)域（比如文字）由于顏色（灰度值）是一致的，因此在水平面（閾值）持續(xù)增長的一段時(shí)間內(nèi)都不會被覆蓋，直到閾值漲到文字本身的灰度值時(shí)才會被淹沒，這些區(qū)域就叫做最大穩(wěn)定極值區(qū)域。
其中，Qi表示閾值為i時(shí)的某一連通區(qū)域，Δ 為灰度閾值的微小變化量，q(i) 為閾值是 i 時(shí)的區(qū)域 Qi 的變化率。
當(dāng)q(i) 為局部極小值時(shí)，則Qi 為最大穩(wěn)定極值區(qū)域。
Detailed Description
Maximally stable extremal region extractor.
The class encapsulates all the parameters of the MSER extraction algorithm (see wiki article).
- there are two different implementation of MSER: one for grey image, one for color image
- the grey image algorithm is taken from: [157] ; 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: [71] ; 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/python/mser.py
cv2.MSER_create()參數(shù)設(shè)置：
_delta 變化量q(i)
_min_area 修剪小于minarea的區(qū)域
_max_area 修剪大于maxArea的面積
_max_variation 修剪該區(qū)域的大小與其子區(qū)域相似
_min_diversity 對于彩色圖像，追溯至截止MSER，其分集小于最小分集
_max_evolution 對于彩色圖像，改進(jìn)的步驟
_area_threshold 對于彩色圖像，區(qū)域閾值導(dǎo)致重新初始化
_min_margin 對于彩色圖像，忽略太小的邊距
_edge_blur_size 邊緣模糊的光圈大小
Full constructor for MSER detector.
Parameters
_delta it compares (sizei?sizei?delta)/sizei?delta
_min_area prune the area which smaller than minArea
_max_area prune the area which bigger than maxArea
_max_variation prune the area have similar size to its children
_min_diversity for color image, trace back to cut off mser with diversity less than min_diversity
_max_evolution for color image, the evolution steps
_area_threshold for color image, the area threshold to cause re-initialize
_min_margin for color image, ignore too small margin
_edge_blur_size for color image, the aperture size for edge blur
cv::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 )
Python:
retval = cv.MSER_create([, _delta[, _min_area[, _max_area[, _max_variation[, _min_diversity[, _max_evolution[, _area_threshold[, _min_margin[, _edge_blur_size]]]]]]]]])
注釋：參考網(wǎng)站
參考官網(wǎng)：
https://docs.opencv.org/3.4/d3/d28/classcv_1_1MSER.html
https://blog.csdn.net/wsp_1138886114/article/details/100135824
