图像处理之Hessian矩阵提取关键点

一：大致的算法流程

1. 对每个像素点计算图像在X方向Y方向的二阶偏导数，计算图像的XY方向的导数

2. 根据第一步的计算结果，有Hessian Matrix计算D(h) = Ixx*Iyy - Ixy*Ixy






其中Ixx表示X方向的二阶偏导数

Iyy表示Y方向的二阶偏导数

Ixy表XY方向的二阶导数

3. 根据第二步计算出来的值使用3×3窗口实现非最大信号压制，

我的做法， 直接给了threshold值，这个其实不很对，真的懒，不想弄啦！

二：导数计算实现

关于一阶与二阶高斯偏导数计算请看这里：

http://blog.csdn.net/jia20003/article/details/16369143

三：程序效果



四：算法代码

package com.gloomyfish.image.harris.corner;

import java.awt.image.BufferedImage;
import java.util.ArrayList;
import java.util.List;

import com.gloomyfish.filter.study.AbstractBufferedImageOp;

public class HessianFeatureDetector extends AbstractBufferedImageOp {

private GaussianDerivativeFilter gdFilter;
private double minRejectThreshold = 4.1; // (r+1)^2/r
private List<HessianMatrix> pixelMatrixList;

public HessianFeatureDetector()
{
gdFilter = new GaussianDerivativeFilter();
pixelMatrixList = new ArrayList<HessianMatrix>();
}

@Override
public BufferedImage filter(BufferedImage src, BufferedImage dest) {
int width = src.getWidth();
int height = src.getHeight();
initSettings(height, width);
if ( dest == null )
dest = createCompatibleDestImage( src, null );

int[] inPixels = new int[width*height];
gdFilter.setDirectionType(GaussianDerivativeFilter.XX_DIRECTION);
BufferedImage bixx = gdFilter.filter(src, null);
getRGB( bixx, 0, 0, width, height, inPixels );
extractPixelData(inPixels, GaussianDerivativeFilter.XX_DIRECTION, height, width);

// YY Direction
gdFilter.setDirectionType(GaussianDerivativeFilter.YY_DIRECTION);
BufferedImage biyy = gdFilter.filter(src, null);
getRGB( biyy, 0, 0, width, height, inPixels );
extractPixelData(inPixels, GaussianDerivativeFilter.YY_DIRECTION, height, width);

// XY Direction
gdFilter.setDirectionType(GaussianDerivativeFilter.XY_DIRECTION);
BufferedImage bixy = gdFilter.filter(src, null);
getRGB( bixy, 0, 0, width, height, inPixels );
extractPixelData(inPixels, GaussianDerivativeFilter.XY_DIRECTION, height, width);

int[] outPixels = new int[width*height];
int index = 0;
for(int row=0; row<height; row++) {
int ta = 0, tr = 0, tg = 0, tb = 0;
for(int col=0; col<width; col++) {
index = row * width + col;
ta = 255;
HessianMatrix hm = pixelMatrixList.get(index);
double[] t = hm.getThreshold();
if(t[0] > minRejectThreshold)
{
tr = 127;
}
else
{
tr = 0;
}
if(t[1] > minRejectThreshold)
{
tg = 127;
}
else
{
tg = 0;
}
if(t[2] > minRejectThreshold)
{
tb = 127;
}
else
{
tb = 0;
}
outPixels[index] = (ta << 24) | (tr << 16) | (tg << 8) | tb;
}
}

setRGB( dest, 0, 0, width, height, outPixels );
return dest;
}

private void initSettings(int height, int width)
{
int index = 0;
for(int row=0; row<height; row++) {
for(int col=0; col<width; col++) {
index = row * width + col;
HessianMatrix matrix = new HessianMatrix();
pixelMatrixList.add(index, matrix);
}
}
}

private void extractPixelData(int[] pixels, int type, int height, int width)
{
int index = 0;
for(int row=0; row<height; row++) {
int ta = 0, tr = 0, tg = 0, tb = 0;
for(int col=0; col<width; col++) {
index = row * width + col;
ta = (pixels[index] >> 24) & 0xff;
tr = (pixels[index] >> 16) & 0xff;
tg = (pixels[index] >> 8) & 0xff;
tb = pixels[index] & 0xff;
HessianMatrix matrix = pixelMatrixList.get(index);
if(type == GaussianDerivativeFilter.XX_DIRECTION)
{
matrix.setXx(new double[]{tr, tg, tb});
}
if(type == GaussianDerivativeFilter.YY_DIRECTION)
{
matrix.setYy(new double[]{tr, tg, tb});
}
if(type == GaussianDerivativeFilter.XY_DIRECTION)
{
matrix.setXy(new double[]{tr, tg, tb});
}
}
}
}

}

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