Fast角點檢測演算法

Mega_Li發表於2018-05-08

AST演算法

1.角點定義

　　角點是一種區域性特徵，具有旋轉不變性和不隨光照條件變化而變化的特點，一般將影象中曲率足夠高或者曲率變化明顯的點作為角點。檢測得到的角點特徵通常用於影象匹配、目標跟蹤、運動估計等方面。

2.Fast檢測角點

1）基本思想

　　E.Rosten和T.Drummond兩位大佬在06年一篇文章中提出了FAST特徵演算法，基本思想十分簡單：以某個畫素點為圓心，某半徑的圓周上其他畫素點與圓心畫素點特性差異達到某種標準時即認為該點就是角點。

2）數學模型

　　經過測試發現，選取的圓形半徑為3時可以兼顧檢測結果和效率。如上圖所示，點p與半徑為3的圓周上的16個畫素點比較其灰度差，若灰度差絕對值大於某閾值的點數超過設定數目（一般可以取9/10/11/12），則認為該點是角點。

使用小技巧加速該演算法，這裡取設定數目為12。

　　（1）判斷點1和點9灰度值與點p差值絕對值是否都大於閾值，如果是則繼續；否則pass該點

（2）判斷點1、點5、點9、點13四點與點p灰度值差值大於閾值的數目是否大於2，如果是則繼續；否則pass該點

（3）判斷圓周上16點與點p灰度值差值大於閾值的數目是否不小於12，如果是則認為該點是候選點；否則pass該點

　　對影象中所有畫素點進行上述操作後，得到候選點點集。通常使用非極大值抑制過濾區域性非極大值點，在這之前需要先計算各候選點的score,score就定義為16個點與中心點灰度值差值的絕對值總和

3.opencv實現

個人使用vs2012+opencv2.4.13作為開發環境，具體實現如下

#include <iostream>
#include <core/core.hpp>
#include <highgui/highgui.hpp>
#include <imgproc/imgproc.hpp>
#include <features2d/features2d.hpp>

using namespace std;
using namespace cv;


int getSum(uchar *p, int length)
{
    int sum = 0;
    for(int i=0;i<length; i++)
    {
        sum += *(p+i);
    }
    return sum;
}

int main(int argc, char* argv[])
{
    /* 1.讀入影象 */
    Mat image = imread("../church01.jpg", 0);
    if(!image.data)
        return 0;

    namedWindow("Original Image");
    imshow("Original Image", image);

    Mat fastImg(image.size(), CV_8U, Scalar(0));//用於儲存Fast特徵點候選點
    Mat fastScore(image.size(), CV_32F, Scalar(0));//用於計算候選點score
    vector<Point> points;
    int rows, cols, threshold;
    rows = image.rows;
    cols = image.cols;
    threshold = 50;

    /* 2.檢測Fast特徵點 */
    for(int x = 3; x < rows - 3; x++)
    {
        for(int y = 3; y < cols - 3; y++)
        {
            uchar delta[16] = {0};
            uchar diff[16] = {0};
            delta[0] = (diff[0] = abs(image.at<uchar>(x,y) - image.at<uchar>(x, y-3))) > threshold;
            delta[8] = (diff[8] = abs(image.at<uchar>(x,y) - image.at<uchar>(x, y+3))) > threshold;
            if(getSum(delta, 16) == 0)
                continue;
            else
            {
                delta[12] = (diff[12] = abs(image.at<uchar>(x,y) - image.at<uchar>(x-3, y))) > threshold;
                delta[4] = (diff[4] = abs(image.at<uchar>(x,y) - image.at<uchar>(x+3, y))) > threshold;
                if(getSum(delta, 16) < 3)
                    continue;

                else
                {
                    delta[1] = (diff[1] = abs(image.at<uchar>(x,y) - image.at<uchar>(x+1, y-3))) > threshold;
                    delta[2] = (diff[2] = abs(image.at<uchar>(x,y) - image.at<uchar>(x+2, y-2))) > threshold;
                    delta[3] = (diff[3] = abs(image.at<uchar>(x,y) - image.at<uchar>(x+3, y-1))) > threshold;
                                
                    delta[5] = (diff[5] = abs(image.at<uchar>(x,y) - image.at<uchar>(x+3, y+1))) > threshold;
                    delta[6] = (diff[6] = abs(image.at<uchar>(x,y) - image.at<uchar>(x+2, y+2))) > threshold;
                    delta[7] = (diff[7] = abs(image.at<uchar>(x,y) - image.at<uchar>(x+1, y+3))) > threshold;

                    delta[9] = (diff[9] = abs(image.at<uchar>(x,y) - image.at<uchar>(x-1, y+3))) > threshold;
                    delta[10] = (diff[10] = abs(image.at<uchar>(x,y) - image.at<uchar>(x-2, y+2))) > threshold;
                    delta[11] = (diff[11] = abs(image.at<uchar>(x,y) - image.at<uchar>(x-3, y+1))) > threshold;

                    delta[13] = (diff[13] = abs(image.at<uchar>(x,y) - image.at<uchar>(x-3, y-1))) > threshold;
                    delta[14] = (diff[14] = abs(image.at<uchar>(x,y) - image.at<uchar>(x-2, y-2))) > threshold;
                    delta[15] = (diff[15] = abs(image.at<uchar>(x,y) - image.at<uchar>(x-1, y-3))) > threshold;

                    if(getSum(delta, 16) >= 12)
                    {
                        points.push_back(Point(y,x));
                        fastScore.at<float>(x,y) = getSum(diff, 16);
                        fastImg.at<uchar>(x,y) = 255;
                    }
                }
            }
        }

    }

    vector<Point>::const_iterator itp = points.begin();
    while(itp != points.end())
    {
        circle(image, *itp, 3, Scalar(255), 1);
        ++itp;
    }
    //未進行非極大值抑制之前的特徵點檢測結果
    namedWindow("Fast Image");
    imshow("Fast Image", image);

    /* 3.對特徵點候選點進行非極大值抑制 */
    image = imread("../church01.jpg", 0);
    Mat dilated(fastScore.size(), CV_32F, Scalar(0));
    Mat localMax;
    Mat element(7, 7, CV_8U, Scalar(1));
    dilate(fastScore, dilated, element);
    compare(fastScore, dilated, localMax, CMP_EQ);
    bitwise_and(fastImg, localMax, fastImg);

    for(int x = 0;x < fastImg.cols; x++)
    {
        for(int y = 0; y < fastImg.rows; y++)
        {
            if(fastImg.at<uchar>(y,x))
            {
                circle(image, Point(x,y), 3, Scalar(255), 1);

            }
        }
    }
    //進行非極大值抑制之後的特徵點檢測結果
    namedWindow("Fast Image2");
    imshow("Fast Image2", image);

    waitKey();
    return 0;
}

程式碼執行結果如下，比較第2/3張圖可以發現，非極大值抑制具有過濾掉區域性區域中非極大值角點的作用。