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So one good step to enhance the vein-like structures is coherence enhancing diffusion: Weickert, Joachim. "Coherence-enhancing diffusion filtering." International Journal of Computer Vision 31.2-3 (1999): 111-127. So I first apply this algorithm to your image, aggressively. The next step is to identify the curvilinear structures, which would in this case ...

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Second question is easy: optical flow, more specifically dense optical flow, is an algorithm that takes two consecutive video frames and returns a vector field. For every pixel in frame 1 you get a vector showing where it moved to in frame 2. You can also have sparse optical flow, which only computes the motion vectors for certain pixels, such as the ...

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I don't think you can completely get rid of histogram or threshold-based binarization, since the former is to achieve line segmentation, while the latter is to extract the letters. The Radon horizontal projection is used for line segmentation, and the center line can be used to approximate the baseline of each segment. Yet this is somehow equivalent to the ...

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Geometric Transformation Supose that $f$ is an image, defined over $(w,z)$ coordinate system, undergoes geometric distortion to produce an image $g$, defined over $(x,y)$ coordinate system. To perform this operation is used this $$(x,y) = T\{(w,z)\}$$ So, one of most commmonly used forms of spatial transformation is the affine transform (Wolberg ). ...

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I am very surprised why no-one mentioned methods of Generalized Hough Transform family. They directly solve this particular problem. Here is what I propose: Take the template and create the R-table, indexing the edges of the template. The edges I select are the following: Use the default OpenCV implementation of generalized Hough transform to obtain: ...

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This is the list I'd recommend: Rafael C. Gonzalez, Richard E. Woods - Digital Image Processing Great introductory book. Well written, a lot of examples. Though it is not deep in any of the fields. Alan C. Bovik - The Essential Guide to Image Processing A comprehensive book on many image processing related subjects. Gret book to skim through. Richard ...

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Except I have an important constraint, the features on the original image must maintain their relative area after the transformation. I'm assuming you're looking for a smooth transformation, where all areas (not just the 4 squares in your image) maintain their relative sizes. IIRC, this constraint is called "fluid registration". (I'm pretty sure I've read ...

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Assume that the center of the square maps to the center of the vertexes. The vertices in the output are known. Assume they are $(x_1,y_1),...,(x_4,y_4)$ Their center is $( \frac{x_1+x_2+x_3+x_4}{4},\frac{y_1+y_2+y_3+y_4}{4})$ Now you have 4 degrees of freedom of where to put the points where the color change. ( w_1 x_1 +(1-w_1) x_2 ,w_1 y_1 +(1-w_1 ) ... 2 I personally like this one. It is nicely designed for occlusions and large displacements. But, recent trends in deep learning lead to better results in optical flow such as this and this. 2 There two ways to look at this problem. In simple terms, image rectification warps both images onto a common coordinate frame by typically estimating the transformation using the epipolar geometry. Image alignment finds the transformation from one image to the other. It doesn't guarantee any constrains on the epipolar geometry and only one single image is ... 2 There is no matrix that maps a pixel in camera 1 to the corresponding pixel in camera 2. This is because the location of the corresponding pixel depends on the 3-D location of the corresponding point in the world. What you have instead is the Fundamental matrix, which maps a pixel in camera 1 to a line in camera 2, called the epipolar line. 2 I have referred to Stage I.D of this tutorial. Hope this helps. http://www.robots.ox.ac.uk/~vgg/practicals/instance-recognition/index.html#stage-id-improving-sift-matching-using-a-geometric-transformation When the features have scale and orientation assigned (e.g. SIFT features have these properties), you can compute similarity transform between each ... 2 To find coordinate or index of drew region, first you have to find the coordinate of 4 colored dots, which is easily obtained by using the transformPointsForward function over the intrinsic coordinate of these 4 points in your original image. intrinsic coordinate is the index of your image matrix. then you have to do some geometric calculations. first, find ... 2 I have found this survey paper very useful: http://ralph.cs.cf.ac.uk/papers/Geometry/Registration.pdf It gives a categorized overview of the field. I think section 3 and 4 are the most relevant ones for you. 2 You need to be careful when reproducing formulas! Youri_r$,$i_w$are actually$\bar{i_r}$,$\bar{i_w}$in the paper and two sentences above$(4)$, it says these are their zero-mean versions, which are obtained by subtracting from each vector its corresponding arithmetic mean. So, the symbols in your formula are always the same, regardless of ... 2 A fundamental book on image processing for electrical engineers is Two-Dimensional Signal and Image Proccesing_Jae S. Lim A highly recommended one, again, for electrical engineers is Fundamentals of Digital Image Processing_Anil Jain A hands-on book on basic practical image procesing is Principles of Digital Image Processing_Wilhelm Burger If ... 2 I am not an expert on mutual information but I understand a fair bit of the mathematics here. So, I read the paper and indeed there seems some typo in the equation$(17)$. I am yet to figure that out. But I have a way for you to compute the derivate as per equation$(17)$. So, if we go back to the equation, then it seems that equation$17$is essentially ... 1 You are trying to achieve something called interpolation and this can be done in roughly one line using MATLAB. Interpolation is well explained over the web so I won't detail the functioning in this answer (Wikipedia page is very complete and redirect to many interpolation methods, here is the MATLAB documentation for 2D interpolation). In a nutshell you ... 1 I though of using a corner detector like harris, but it gives me many additional corners (as the sides of the shapes are not really straight due to discretization. In that case you should keep only the strongest feature points. the corners may actually be lost and look like a curved line as shown below, even though the image before segmentation had ... 1 I am unable to comment on your Matlab code, but eq. (14) seems straightforward to me. You have a phase difference field$\theta$which depends on the two spatial wavenumber components$k_1$and$k_2$, and which formes a wrapped plane. The shift you seek is the slope of this plane, expressed as two scalar components$a$and$b$. Since phase wraps, the ... 1 I feel there is no the best method to solve this, but if you do not have some specific feature point to track, I suggest using optical flow a below: compute optical flow of two frames, find the dominate global optical vector, may be through computing average or median of optical vectors (you do not want small noisy motions) Select at least 2 vectors ... 1 Formally speaking, you would like to extrinsically calibrate the laser scanner to the 2D image. I have taken the liberty to edit your question to reflect that. Here is how my initial approach would be: Calibrate the intrinsics of the 2D camera. For that, just use OpenCV. You should store the intrinsic parameters: focal lengths, principal point and ... 1 For blurred images, for which I mean the blur kernels are different for the two images, the PC algorithm can also be extended to handle this case. Please refer to: PEDONE M, FLUSSER J, HEIKKILA J. Blur invariant translational image registration for N-fold symmetric blurs. [J]. IEEE transactions on image processing, 2013, 22(9): 3676–89. OJANSIVU V, HEIKKILÄ ... 1 The effect of circularity could be decreased by bigger border filled up with zeros (up to width of image). But the method is not perfect inherently, it works perfectly just for circular shift of image. Otherwise the method can fail, mainly when one part of image is very light (high values of pixels). The rest of image should have as uniform distribution of ... 1 There are two simple approaches. Find the hand region with thresholding and then use something like convexity defects. Even though you will have many spurious landmarks at the end, you could easily cluster them out, as your scene is well controlled. There are also couple of youtube videos, if you like to get an idea in advance, , . Fit a hand model ... 1 Take a look to the ImageJ plugin called TurboReg algorithm (or StackReg). It should be really effective for such images. However, in your example, the two pictures seem to have been taken from different angles (right left), but also different height. So two angles are different, and then you have to find an affine transformation. 1 This Process is Known as Color Correction.It is Explained in more detail here. To Give a short Explanation: Lets suppose You have a Input Color Matrix represented as | R_O1 G_O1 B_O1 | A = | R_O2 G_O2 B_O2 | | R_On G_On B_On | and a Reference Target Color Matrix represented as | R_T1 G_T1 B_T1 | B = | R_T2 G_T2 B_T2 | | R_Tn G_Tn ... 1 I can't help with the fisheye lens issue, but assuming that effect isn't too large, the mapping between the above images is a perspective transformation. We can transform a point on the plane$z=1$into 3D space using a$3\times 3$matrix.$\$\left( \begin{array}{c} X\\ Y\\ Z\\ \end{array} \right) = \left( \begin{array}{c} s_x\ \ h_x\ \ t_x \\ ...

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I'm not sure what is meant here by the "optimal" similarity metric for a given situation. The textbook you linked to references the following thesis:  P. A. Viola, Alignment by maximization of mutual information. Ph.D. thesis, Massachusetts Institute of Technology, 1995. ...but I can't find it anywhere online after a quick search. From my point of ...

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The method used by TI in their white paper uses some fancy processing to measure the phase difference, but the underlying principle is reasonably simple. Each pulse of infrared travels at the speed of light. If you place an object right in front of the camera, then the pulses of light reflected back will be in phase with those transmitted - in other words, ...

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