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12

Simple: the Euclidean distance completely ignores the shape when finding a path from the start point to the end point while, for the geodesic distance, the path is constrained to be within the given shape. That's why the distances at the bottom left of the figure are so different.


12

If I understand your method 1 correctly, with it, if you used a circularly symmetrical region and did the rotation about the center of the region, you would eliminate the region's dependency on the rotation angle and get a more fair comparison by the merit function between different rotation angles. I will suggest a method that is essentially equivalent to ...


9

To be honest, most people end up doing something else other than what they directly study. The career follows the job you get. Get a good background in your departments core strengths. you really can’t anticipate what skills you will eventually develop with precision. Soft skills are actually important.


6

Common Approaches for Commercial Denoisers Commercial denoisers are different than what you'd see on most papers. While on papers the results are mostly using objective metrics (PSNR / SSIM) and are evaluated vs. Additive White Gaussian Noise (AWGN) with high level of noise real world images are mostly with moderate level of noise with Mixed Poisson ...


5

Digital image processing is an extension of digital signal processing and linear system theory into two dimensional signals. Image processing involves all low level tasks such as filter design and filtering, spatial scaling, sampling, intensity manipulations, geometry manipulations, Fourier analysis and spectrum analysis, motion estimation, noise reduction, ...


4

It's a DFT property that if you apply DFT twice to input data, you get the original signal flipped (circularly). Stated mathematically for 1D case: $$ x[n] \xrightarrow{ N-DFT } X[k] $$ $$ X[k] \xrightarrow{ N-DFT } Y[k] = N x[-k] $$ similar result can be shown for 2D case. And as you can see, the resulting output is the flipped (horizontally and ...


4

They are both highpass type filters, but used with very different intentions. One should immediately observe the fundamental difference that the output of unsharp masking filter is an enhanced image to be viewed by humans, whereas the output of the Sobel (edge detector) filter is not an image to be viewed by humans, but rather a description of the edges to ...


4

The result of a convolution of a data vector of length M with a kernel of length G is of length M + G - 1. (the maximum length of the non-zero portion, even though the limits of integration is sometimes written as from -infinity to +infinity) This is clearly (G - 1) elements longer than the original data vector. So where do these new, "extra", additional ...


4

There is a similar DSP trick here, but I don't remember the details exactly. I read about it somewhere, some while ago. It has to do with figuring out fabric pattern matches regardless of the orientation. So you may want to research on that. Grab a circle sample. Do sums along spokes of the circle to get a circumference profile. Then they did a DFT on ...


4

I myself recently graduated from Applied Mathematics and began PhD in signal processing. I do Stochastic Geometry modeling of wireless networks in particular, which is quite mathematical subject. It involves measure theory, probability theory, Fourier Analysis etc. etc. The area of Signal Processing is very broad indeed. It of course depends if you want to ...


4

most of the people who answer questions here tend to use python or matlab. you are more likely to get help here using those tools rather than labview. in of itself, this doesn’t say matlab is “better”. it all depends on what is better for you. in the distant past, matlab was restricted to double floats for all numerical representations. this made ...


3

I replicated the algorithm perfectly in MATLAB (Based on @Ivan Kuckir answer): function [ mO ] = ApplyBlackWhiteFilter( mI, vCoeffValues ) FALSE = 0; TRUE = 1; OFF = 0; ON = 1; numRows = size(mI, 1); numCols = size(mI, 2); dataClass = class(mI); numCoeff = size(vCoeffValues, 1); hueRadius = 1 / numCoeff; vHueVal = [0:(numCoeff - 1)] * ...


3

First, a warm welcome to SE! Basically, you have a calibrated 3D reconstruction problem. The typical approach follows a 5-stage pipeline: Identify 2D features in each image along with the associated descriptors. Algorithms such as SURF, SIFT or AKAZE are heavily used and are available in many vision libraries such as OpenCV. Match the extracted keypoints ...


3

The continuous Gaussian, whatever its dimension (1D, 2D), is a very important function in signal and image processing. As most data is discrete, and filtering can be costly, it has been, and still is, subject of quantities of optimization and quantification/quantization schemes. In one 1D, the three most direct for a finite-length filter are illustrated ...


3

We need to assume the reader knows some basic stuff to answer that. Let's give it a try. Lets understand the sentence - Zero / First Order Hold. We have the Zero / First Order and the Hold. Zero / First Order hold means the order of the Taylor Series of the function we use to interpolate. In other words, the degree of the Polynomial we can write the ...


3

Why Does 2D FFT of Gaussian Looks More Sharper than Gaussian Itself? Have a look at the Fourier Transfrom of a Gaussian Signal. $$ \mathcal{F}_{x} \left\{ {e}^{-a {x}^{2} } \right\} \left( \omega \right) = \sqrt{\frac{\pi}{a}} {e}^{- {\pi}^{2} \frac{ {\omega}^{2} }{a} } $$ First, Gaussian Signal stays Gaussian under Fourier Transform. As you can see, the ...


3

This is a good question and something that I remember asking myself when I first learned about impulse responses and convolution. To understand this, it is first necessary to understand the significance of impulses and impulse responses. Referring to the image below, you can see that an impulse is an instantaneous like input and the impulse response is the ...


3

Focus on the first equation for EY. Back in the day when color television was being developed, the color signal had to be compatible with black and white TVs and vice versa. So the compatible brightness signal (luma Y) has to be calculated from the three primary color signals (R, G B) for transmission. Human visual system does not perceive brightnesses of ...


3

I've went ahead and basically adjusted the Hough transform example of opencv to your use case. The idea is nice, but since your image already has plenty of edges due to its edgy nature, the edge detection shouldn't have much benefit. So, what I did above said example was Omit the edge detection decompose your input image into color channels and process ...


3

Approximation by the real part of a weighted sum of separable complex Gaussian component kernels Figure 1. The proposed scheme illustrated as 1-d real convolutions ($*$) and additions ($+$), for cut-off frequency $\omega_c = \pi/4$ and kernel width $N=41$. Each the upper and the lower half of the diagram is equivalent to taking the real part of a 1-d ...


3

In support of Comparable mixin, a default <=> or spaceship operator for pixels is defined in the function Pixel_spaceship in rmpixel.c. However, in your use of the sort method, you define your own code block that overrides the <=> operator, and yours takes a single argument rather than two which would be correct, so the definition is broken and ...


3

Your interpretation is correct: directional derivation operators highlight variation in a given direction. Here, you use the $2$-point discrete derivative in the $x$-direction (along image rows). It may emphasize vertical features. First, such operators indeed extend the initial image range. However, one often uses the absolute value of the derivative to ...


3

MATLAB is one of the most important software inventions of the twentieth century, from a DSP point of view its syntax is simply the best in the world. And image processing is one of its strongest parts. However it's mainly of academic focus and if you look for industrial output you should consider having a number of additional tools. LabView is one such ...


3

KF is actually a mixture of a deterministic state propagator and a statistical estimator. Despite it's name including the term filter, Kalman filter is not a simple frequency selective one. It's indeed a statistical recursive estimator of a state of a (linear) dynamic system. Yet on a broader sense it's called as a filter as it will separate a desired ...


3

Well, look at your original picture: it's constant for all points but the edges, which means your derivative is zero for all points but these edges. By applying a "rounding, smoothing" filter to it, you "smear" the edges enough to make the derivative be non-zero for multiple pixels, in every direction.


2

Because wo want to get the centroid of the image(a block/patch) by the intensity. m00:p = q = 0,sum the intensity matrix. m10:p =1,q = 0,sum of the x-direction. m01:p = 0,q = 1,sum of the y-direction. (m10/m00,m01/m00) is the centroid.


2

Well, yes. In the same way that the development in higher level programming languages like C++ and Python 'killed' assembly programming. That does not mean it is irrelevant to learn assembly when you enroll in a CS course however. It provides great insight in how the computer works, what goes on behind the scenes of higher level languages, what the basic ...


2

Histograms of images can differ, widely. However, when features are inspected, one often uses derivative filters at different scales, or morphological decompositions, or independent component analysis. A traditional and heuristic model for the resulting coefficients of a component is that of the Generalized Gaussian-Laplacian Distribution, or GGD: $$ C_{...


2

In my StackExchange Signal Processing Q38542 GitHub Repository you will be able to see a code which implements 2D Circular Convolution both in Spatial and Frequency Domain. Pay attention to the function CircularExtension2D(). This function align the axis origin between the image and the kernel before working in the Frequency Domain. Remember that for ...


2

You can use the following variance of Laplacian responses: cv2.Laplacian(gray_image, cv2.CV_64F).var() More details at https://www.pyimagesearch.com/2015/09/07/blur-detection-with-opencv/


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