opencvヒストグラムを使ってイメージブロックの平均をどうやって決めるのですか？

ヒストグラムを使って画像の平均ブロックを決定したいと思います。私のイメージが64×64次元であるとしましょう。私はそれを4×4ブロックに分割し、各ブロックの平均を決定する必要があります（言い換えれば、今は4ブロックになります）。

opencvを使用して、IplImageを利用してヒストグラムビンを使ってブロック平均を決定する方法を教えてください。

以下のコードは画像全体の平均を決定するためにopencvヒストグラムです：

int i, hist_size = 256;
float max_value,min_value;
float min_idx,max_idx;
float bin_w;
float mean =0, low_mean =0, high_mean =0, variance =0;

float range_0[]={0,256};
float *ranges[]={range_0};

IplImage* im = cvLoadImage("killerbee.jpg");
//Create a single planed image of the same size as the original
IplImage* grayImage = cvCreateImage(cvSize(im->width,im->height),IPL_DEPTH_8U, 1);
//convert the original image to gray
cvCvtColor(im, grayImage, CV_BGR2GRAY);

/* Remark this, since wanna evaluate whole area.
    //create a rectangular area to evaluate
    CvRect rect = cvRect(0, 0, 500, 600 );
    //apply the rectangle to the image and establish a region of interest
    cvSetImageROI(grayImage, rect);
End remark*/

//create an image to hold the histogram
IplImage* histImage = cvCreateImage(cvSize(320,200), 8, 1);
//create a histogram to store the information from the image
CvHistogram* hist = cvCreateHist(1, &hist_size, CV_HIST_ARRAY, ranges, 1);
//calculate the histogram and apply to hist
cvCalcHist( &grayImage, hist, 0, NULL );

//grab the min and max values and their indeces
cvGetMinMaxHistValue( hist, &min_value, &max_value, 0, 0);
//scale the bin values so that they will fit in the image representation
cvScale( hist->bins, hist->bins, ((double)histImage->height)/max_value, 0 );

//set all histogram values to 255
cvSet( histImage, cvScalarAll(255), 0 );
//create a factor for scaling along the width
bin_w = cvRound((double)histImage->width/hist_size);

for( i = 0; i < hist_size; i++ ) {
    //draw the histogram data onto the histogram image
    cvRectangle( histImage, cvPoint(i*bin_w, histImage->height),cvPoint((i+1)*bin_w,histImage->height - cvRound(cvGetReal1D(hist->bins,i))),cvScalarAll(0), -1, 8, 0 );
    //get the value at the current histogram bucket
    float* bins = cvGetHistValue_1D(hist,i);
    //increment the mean value
    mean += bins[0];
}
//finish mean calculation
mean /= hist_size;

//display mean value onto output window
cout<<"MEAN VALUE of THIS IMAGE : "<<"\n";

//go back through now that mean has been calculated in order to calculate variance
for( i = 0; i < hist_size; i++ ) {
    float* bins = cvGetHistValue_1D(hist,i);
    variance += pow((bins[0] - mean),2);
}
//finish variance calculation
variance /= hist_size;
cvNamedWindow("Original", 0);
cvShowImage("Original", im );

cvNamedWindow("Gray", 0);
cvShowImage("Gray", grayImage );

cvNamedWindow("Histogram", 0);
cvShowImage("Histogram", histImage );

//hold the images until a key is pressed
cvWaitKey(0);

//clean up images
cvReleaseImage(&histImage);
cvReleaseImage(&grayImage);
cvReleaseImage(&im);

//remove windows
cvDestroyWindow("Original");
cvDestroyWindow("Gray");
cvDestroyWindow("Histogram");

本当にありがとうございます。