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The solution is to use an "homography" / aka "projective transformation" (see this PDF, page 16). Here is a working code showing how to do it with Python + OpenCV.


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I'll simply present a series of results relevant to this question. Note that eigenvalues of this matrix are non-negative Let $x = [u\ v]^T$ $$x^TMx = [u\ v] \begin{bmatrix} I_x^2 & I_x I_y \\ I_x I_y & I_y^2 \end{bmatrix} \begin{bmatrix} u \\ v \end{bmatrix} = u^2 I_x^2 + 2uv I_x I_y + v^2 I_y^2 = (uI_x + vI_y)^2 \geq 0$$ Hence $M$ is positive-...


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A couple of confusions here. Features refer to some form of lower dimensional descriptors that explain a (potentially local) region of interest. They are useful in converting the appearance into certain signatures that are easier to handle / more robust than naively using pixels. What you are trying to ask is probably keypoint detection. This term is used ...


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Threshold the image to convert into binary image. Check whether it is a corner or not by dilating with these kernels separately on corners detected by corner detection. $$E_1 = \begin{bmatrix}0 & 0 &0 & 1& 0\\0 & 0 & 0 & 1& 0\\0 & 0 & 0 & 1& 0\\1 & 1 & 1 & 1& 0\\0 & 0 & 0 & 0& 0\...


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