template-matching

I am working on a project of recognizing TV Channels. I am taking photos of the channels suck that i try to avoid the background and to take the sample from the center of the logo. I recognize 4 different logos, here are the templates: How does my template matching algorithm work: Given 4 templates of size 100x100, each representing a different TV Channel, each having a different threshold (of probability). The user is capturing the logo from the TV set, and then the algorithm is: - Run 4 independent template matching on each template to receive the probability for each template to match the

I am trying to implement real-time tracking using templates. I wish to update the template with every frame. The main modifications I have done are: 1) separated the template matching and minmaxLoc into separate modules namely, TplMatch() and minmax() functions, respectively. 2) Inside the track() function, the select_flag is kept always true so that new template is copied to 'myTemplate' with every iteration. 3) The last 3 lines of function track() are to update the template (roiImg). 4) Also, I have removed any arguments to track() function, since, img and roiImg are global variables and

One whole day I have tried a lot to get all the related matches (with matchtemplate function) in sub-Image , which is ROI i have already extracted from the original image with the mousecallback function. So my code is below for the Matchingfunction ////Matching Function void CTemplate_MatchDlg::OnBnTemplatematch() { namedWindow("reference",CV_WINDOW_AUTOSIZE); while(true) { Mat ref = imread("img.jpg"); // Original Image mod_ref = cvCreateMat(ref.rows,ref.cols,CV_32F);// resizing the image to fit in picture box resize(ref,mod_ref,Size(),0.5,0.5,CV_INTER_AREA); Mat tpl =imread("Template.jpg"); /

What I've done I have a small template image which is meant to be used to find coordinates of matching subimages within a larger screenshot image. The screenshot itself is captured into a memory DC with the help of BitBlt , then converted into a cv::Mat via GetDIBits , like so: HDC windowDc = GetWindowDC(hwndTarget); HDC memDc = CreateCompatibleDC(windowDc); // ... HBITMAP hbmp = CreateCompatibleBitmap(windowDc, width, height); SelectObject(memDc, hbmp); BITMAPINFOHEADER bi = { sizeof(BITMAPINFOHEADER), // biSize width, // biWidth -height, // biHeight 1, // biPlanes 32, // biBitCount BI_RGB, /

I'm using OpenCV 2.3 for keypoints detection and matching. But I am a bit confused with the size and response parameters given by the detection algorithm. What do they exactly mean? Based on the OpenCV manual, I can't figure it out: float size : diameter of the meaningful keypoint neighborhood float response : the response by which the most strong keypoints have been selected. Can be used for further sorting or subsampling I thought the best point to track would be the one with the highest response but it seems that it is not the case. So how could I subsample the set of key points returned by