OpenCV中的照明标准化

问题描述

我正在研究人脸识别项目.我的图片具有不同的照明，因此我需要进行照明归一化.我读了一篇声称进行照度归一化的论文.本文介绍了以下功能和值.

I am working on a face recognition project. I have pictures with different lighting so I need to do illumination normalization. I read a paper which which claims to do illumination normalization. The paper describe the following function and values.

1-使用gamma = 0.2的gamma校正
2-(sigma0 = 1，sigma1 = 2)的高斯(DOG)滤波的差
3-对比度均衡(本文使用截断阈值为10且压缩分量为0.1)

1- gamma correction with gamma = 0.2
2- Difference of Gaussian (DOG) filtering with (sigma0 = 1, sigma1 =2)
3- contrast equalization (truncation threshold of 10 and compressive component 0.1 is used in the paper)

我使用CvPow进行伽玛校正，使用CvSmooth进行DoG，使用Threshold()进行截断(我不知道如何指定压缩分量)，但是我没有得到确切的图像.我使用直方图均衡进行对比度均衡.

I use CvPow for gamma correction, CvSmooth for DoG and Threshold() with truncate (I don't know how to specify the compression component) but I didn't get the exact image. I used histogram equalization for contrast equalization.

如果有人曾经做过或有任何想法?

If someone has done it before or has any idea??

论文链接: http://lear.inrialpes.fr /pubs/2007/TT07/Tan-amfg07a.pdf

下面的代码:(Peb Aryan的Python代码转换为JAVACV)

The code is below: (Python code of Peb Aryan converted to JAVACV)

public static IplImage preprocessImg(IplImage img)
{
    IplImage gf = cvCreateImage(cvSize(img.width(),img.height()),IPL_DEPTH_32F, 1 );
    IplImage gr = IplImage.create(img.width(),img.height(), IPL_DEPTH_8U, 1);
    IplImage tr = IplImage.create(img.width(),img.height(), IPL_DEPTH_8U, 1);

    IplImage b1 = IplImage.create(img.width(),img.height(),IPL_DEPTH_32F, 1 );
    IplImage b2 = IplImage.create(img.width(),img.height(),IPL_DEPTH_32F, 1 );
    IplImage b3 = IplImage.create(img.width(),img.height(),IPL_DEPTH_32F, 1 );
    CvArr mask = IplImage.create(0,0,IPL_DEPTH_8U, 1 );

    cvCvtColor(img, gr, CV_BGR2GRAY);
    gamma(gr,gr,gf);

    cvSmooth(gf,b1,CV_GAUSSIAN, 1);
    cvSmooth(gf,b2,CV_GAUSSIAN,23);
    cvSub(b1,b2,b2,mask);
    cvConvertScale(b2,gr,127,127);
    cvEqualizeHist(gr, gr);

    //cvThreshold(gr,tr,255,0,CV_THRESH_TRUNC);

    return gr;
}

public static void gamma(IplImage src,IplImage dst, IplImage temp)
{
    cvConvertScale(src,temp, 1.0/255,0);
    cvPow(temp, temp, 0.2);
    cvConvertScale(temp, dst, 255,0);
}

这是我尝试的结果:

以及论文参考:

推荐答案

对您来说是否为时已晚.

Don't know if it's too late for you.

在原始论文中，DoG是由给定的sigma执行的，此处半径(23)太大.尝试radius = 7和radius =1.关于均衡步骤，它与纸张不同.您需要自己实施.

In the original paper, DoG was performed by a given sigma, here your radius(23) it too big. Try radius = 7 and radius = 1. About the equalization step, it's different from the paper. you need implement one by yourself.

顺便说一句:某些cvSmooth之类的基本功能未在您的应用程序中实现 right .您可能需要自己实施才能获得更好的结果.

BTW: some basic functions like cvSmooth was not implemented right for your application. You probably need to implement by yourself to get a better result.

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