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OpenCV Tutorial

This tutorial discusses a simple image correlation example in OpenCV, demonstrating the creation of result images using different matching techniques.

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OpenCV Tutorial

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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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