Shi-Tomasi 算法是Harris 算法的改进。

Harris 算法最原始的定义是将矩阵 M 的行列式值与 M 的迹相减，再将差值同预先给定的阈值进行比较。后来Shi 和Tomasi 提出改进的方法，若两个特征值中较小的一个大于最小阈值，则会得到强角点。

void goodFeaturesToTrack(InputArray image, OutputArray corners, int maxCorners, double qualityLevel, doubleminDistance, InputArray mask=noArray(), int blockSize=3, bool useHarrisDetector=false, double k=0.04 )

Parameters:

• image – Input 8-bit or floating-point 32-bit, single-channel image.
• corners – Output vector of detected corners.
• maxCorners – Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned.
• qualityLevel – Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see cornerMinEigenVal() ) or the Harris function response (see cornerHarris() ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
• minDistance – Minimum possible Euclidean distance between the returned corners.
• mask – Optional region of interest. If the image is not empty (it needs to have the type CV_8UC1and the same size as image ), it specifies the region in which the corners are detected.
• blockSize – Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs() .
• useHarrisDetector – Parameter indicating whether to use a Harris detector (see cornerHarris()) or cornerMinEigenVal().
• k – Free parameter of the Harris detector.

论文看过之后过来补充 ~~

Code

#include "stdafx.h"#include "opencv2/highgui/highgui.hpp"#include "opencv2/imgproc/imgproc.hpp"#include <iostream>#include <stdio.h>#include <stdlib.h>using namespace cv;using namespace std;/// Global variablesMat src, src_gray;int maxCorners = 23;int maxTrackbar = 100;RNG rng(12345);char* source_window = "Image";/// Function headervoid goodFeaturesToTrack_Demo( int, void* );/** * @function main */int main( int argc, char** argv ){  /// Load source image and convert it to gray  src = http://www.mamicode.com/imread("xue.jpg", 1 );  cvtColor( src, src_gray, CV_BGR2GRAY );  /// Create Window  namedWindow( source_window, CV_WINDOW_AUTOSIZE );  /// Create Trackbar to set the number of corners  createTrackbar( "Max  corners:", source_window, &maxCorners, maxTrackbar, goodFeaturesToTrack_Demo );  imshow( source_window, src );  goodFeaturesToTrack_Demo( 0, 0 );  waitKey(0);  return(0);}/** * @function goodFeaturesToTrack_Demo.cpp * @brief Apply Shi-Tomasi corner detector */void goodFeaturesToTrack_Demo( int, void* ){  if( maxCorners < 1 ) { maxCorners = 1; }  /// Parameters for Shi-Tomasi algorithm  vector<Point2f> corners;  double qualityLevel = 0.01;  double minDistance = 10;  int blockSize = 3;  bool useHarrisDetector = false;  double k = 0.04;  /// Copy the source image  Mat copy;  copy = src.clone();  /// Apply corner detection  goodFeaturesToTrack( src_gray,               corners,               maxCorners,               qualityLevel,               minDistance,               Mat(),               blockSize,               useHarrisDetector,               k );  /// Draw corners detected  cout<<"** Number of corners detected: "<<corners.size()<<endl;  int r = 4;  for( int i = 0; i < corners.size(); i++ )     { circle( copy, corners[i], r, Scalar(rng.uniform(0,255), rng.uniform(0,255),              rng.uniform(0,255)), -1, 8, 0 ); }  /// Show what you got  namedWindow( source_window, CV_WINDOW_AUTOSIZE );  imshow( source_window, copy );}

OpenCV Tutorials —— Shi-Tomasi corner detector