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Image Correlation Tutorial with OpenCV

Learn to perform image correlation in OpenCV using template matching techniques for better results. Extract regions, create templates, apply match functions, and analyze results in this walkthrough tutorial.

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Image Correlation Tutorial with OpenCV

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  1. OpenCV Tutorial Part 3 Image Correlation Gavin S Page gsp8334@cs.rit.edu

  2. Tasks Steps Performed After learning to work with images it is important to learn some of the accessory functions OpenCV has to offer. This tutorial will discuss a simple image correlation example. At this point loading an image and converting it to grayscale should be a simple task and can be copied from past tutorials. Gavin S Page gsp8334@cs.rit.edu

  3. Specify Region Determine the starting point and the size of the region and create the CvRect. Extract Template Region //define the starting point and size of rectangle int xVal = 1145; int yVal = 890; int neighLength = 25; CvRect rect = cvRect(xVal,yVal,neighLength,neighLength); //create the template and extract it from the source image CvMat* tplate = cvCreateMat(neighLength, neighLength, CV_8UC1); cvGetSubRect(imG, tplate, rect ); Here the template region is specified and extracted. Use cvGetSubRect to copy the template from the region. Gavin S Page gsp8334@cs.rit.edu

  4. Create Result Images The image targets for the result of the match function have to be of size W-w+1×H-h+1 and of type 32-bit single channel floating point. Use Template Match //specify the size needed by the match function int resultW = imG->width - tplate->width + 1; int resultH = imG->height - tplate->height +1; //create each of the result images IplImage* result0 = cvCreateImage(cvSize(resultW, resultH), IPL_DEPTH_32F, 1); IplImage* result1 = cvCreateImage(cvSize(resultW, resultH), IPL_DEPTH_32F, 1); IplImage* result2 = cvCreateImage(cvSize(resultW, resultH), IPL_DEPTH_32F, 1); IplImage* result3 = cvCreateImage(cvSize(resultW, resultH), IPL_DEPTH_32F, 1); IplImage* result4 = cvCreateImage(cvSize(resultW, resultH), IPL_DEPTH_32F, 1); IplImage* result5 = cvCreateImage(cvSize(resultW, resultH), IPL_DEPTH_32F, 1); //apply each of the matching techniques cvMatchTemplate(imG, tplate, result0, CV_TM_SQDIFF); cvMatchTemplate(imG, tplate, result1, CV_TM_SQDIFF_NORMED); cvMatchTemplate(imG, tplate, result2, CV_TM_CCORR); cvMatchTemplate(imG, tplate, result3, CV_TM_CCORR_NORMED); cvMatchTemplate(imG, tplate, result4, CV_TM_CCOEFF); cvMatchTemplate(imG, tplate, result5, CV_TM_CCOEFF_NORMED); Apply each of the match techniques for the example. This slide documents the creation of the target images and the usage of the cvMatchTemplate function. Gavin S Page gsp8334@cs.rit.edu

  5. Original Image and Template The original, grayscale image with template. Notice the region from which the template was extracted is labeled in the image. Gavin S Page gsp8334@cs.rit.edu

  6. Poor Results CCOEFF CCORR These particular methods did not demonstrate good results. SQDIFF Gavin S Page gsp8334@cs.rit.edu

  7. CCOEFF_NORMED Notice the high values on the circular letters on the sign Good Results Gavin S Page gsp8334@cs.rit.edu

  8. CCORR_NORMED Notice the high values on the circular letters on the sign Good Results Gavin S Page gsp8334@cs.rit.edu

  9. SQDIFF_NORMED Notice the low values on the circular letters on the sign Good Results Gavin S Page gsp8334@cs.rit.edu

  10. Final This tutorial illustrated a simple example of image correlation. It showed that the normalized techniques exhibited better results Gavin S Page gsp8334@cs.rit.edu

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