template-matching

Here is an example of a code that works perfectly: #include<iostream> #include<vector> template< class D, template< class D, class A > class C, class A = std::allocator< D > > void foo( C< D, A > *bar, C< D, A > *bas ) { std::cout << "Ok!" << std::endl; } int main( ) { std::vector< int > *sample1 = nullptr; std::vector< int > *sample2 = nullptr; foo( sample1, sample2 ); return( 0 ); } In the code below, however, the compiler is unable to match std::vector< int >* with nullptr for the second parameter, even being able to deduct the template types from the first parameter. #include<iostream>

I had a problem where I need to search for a pattern (present as a numpy ndarray) within another image (also present as a numpy ndarray) and compute a template match (minimum difference position in the image). My question is... is there any in-built image that I can possibly use in the Python Imaging Library or Numpy or anything possible that can do this without me manually writing a function to do so??? Thank you.... This is likely best done as an inverse convolution or correlation. Numpy/scipy has code to do both. edit: including a little example. Go here for the ipython notebook file: http:

OpenCV has the matchTemplate() function, which operates by sliding the template input across the output, and generating an array output corresponding to the match. Where can I learn more about how to interpret the six TemplateMatchModes ? I've read through and implemented code based on the tutorial , but other than understanding that one looks for minimum results for TM_SQDIFF for a match and maximums for the rest, I don't know how to interpret the different approaches, and the situations where one would choose one over another. For example (taken from the tutorial) res = cv.matchTemplate(img

I'm trying to match an image with the camera input in Android using template matching. When i try this with static 2 images like in here: OpenCV Template Matching example in Android , everything works just fine. But when I try to use the captured images from the camera, I do not get the correct result. Following is the code that I have written: String baseDir = Environment.getExternalStorageDirectory().getAbsolutePath(); Mat img = Highgui.imread(baseDir + "/mediaAppPhotos/img2.png"); Mat templ = Highgui.imread(baseDir+ "/mediaAppPhotos/chars.png"); int result_cols = img.cols() - templ.cols() +

Anybody know about the fastest method for calculating convolution? Unfortunately the matrix which I deal with is very large (500x500x200) and if I use convn in MATLAB it takes a long time (I have to iterate this calculation in a nested loop). So, I used convolution with FFT and it is faster now. But, I am still looking for a faster method. Any idea? chappjc If your kernel is separable, the greatest speed gains will be realized by performing multiple sequential 1D convolutions. Steve Eddins of MathWorks describes how to take advantage of the associativity of convolution to speed up convolution

I have this image of a table (seen below). And I'm trying to get the data from the table, similar to this form (first row of table image): rows[0] = [x,x, , , , ,x, ,x,x, ,x, ,x, , , , ,x, , , ,x,x,x, ,x, ,x, , , , ] I need the number of x's as well as the number of spaces. There will also be other table images that are similar to this one (all having x's and the same number of columns). So far, I am able to detect all of the x's using an image of an x. And I can somewhat detect the lines. I'm using open cv2 for python. I'm also using a houghTransform to detect the horizontal and vertical

I have a template image , with white background and black shape over it.I also have an image which have a similar shape but in different rotation and scale,also have some noise in image.Just like the pic as follow! I want to use a rotateRect to get a Rect which includes the template shape and use the template to find a rotateRect which includes the target sharp.I have known matchTemplate() is not good when object is rotated or scaled in scene.So I try to use matchShapes() function.But matchShapes() function is used to compare with two contours.So can see the shape in target image,the shape is

I am working with normxcorr2 function in Matlab for template matching. However, what I want to do is different from what normxcorr2 does. The built-in normxcorr2 computes cross-correlation taking into account all the pixels in a rectangular template. But I only want certain pixels to participate in the normalized cross correlation process. For example, I want only the ring-like white region in the following image to be used as a template while correlating. (the black region inside should not be used for computation) And thus if I correlate the above template with the following image, I am