# Tag Info

## Hot answers tagged image-processing

3

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 ...

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 ...

2

Just write down the convolution sum to see what's going on: $$y[n]=\sum_{k=-\infty}^{\infty}x[k]p[n-k]\tag{1}$$ where we define the elements of the sequences $x[n]$ and $p[n]$ as equal to zero for the index $n$ outside the range of non-zero values, i.e., outside $n\in[0,2]$. Taking into account those zero values, we can rewrite $(1)$ with finite summation ...

2

Rather performance intensive, but should get you accuracy as wanted: Edge detect the image Hough transform to a space where you have enough pixels for the wanted accuracy. Because there are enough orthogonal lines; the image in the hough space will contain maxima lying on two lines. These are easily detectable and give you the desired angle.

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Excerpted from Jae S.Lim 2D signal and image processing ch.1, as an example of $2$-D circularly symmetric lowpass filter with a cutoff frequency of $\omega_c$ radians per sample, whose impulse response is given by: $$h[n_1,n_2] = \frac{\omega_c}{2\pi \sqrt{n_1^2 + n_2^2} } J_1 \big( \omega_c \sqrt{n_1^2 + n_2^2} \big)$$ where $J_1$ is the Bessel function ...

2

In the context of image processing (and machine vision as well), blurring is an operation that reduces the sharpness of an image by some lowpass filtering applied on it. There are different causes of blurring such as lens blur, motion blur, or just LSI (linear shift invariant) lowpass filtering. Deblurring refers to any restoration performed on the image ...

2

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 ...

1

Let me present the following Diagram: So, both Deblurring and Deconvolution are operations within the family of Image Restoration (Which is a subset of Inverse Problem set). Basically we build the Image Restoration set by different Degradation Models. The one related to the question are: Linear Degradation Model Namely, the degradation is made by a Linear ...

1

Yes it's true; there are image filter kernels whose coefficients may change with location of the processing and further based on image data. They're known as shift-varying filters. And data dependency also makes them nonlinear. One prime application of shift-varying nonlinear filtering is on the edge adaptive, noise reduction where the kernel coefficients ...

1

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 ...

1

A 2D FFT consists in comparing 2D data with a discrete set of evenly spaced 2D complex exponentials $e_{u,v}$ (made of a cosine for the real part, and and a sine for the imaginary part). Behind the scene, this comparison is exact: a comparison of a 2D data with enough 2D complex exponentials bears the same information as the original data, albeit easier to ...

1

The distinction is whether your x-axis is in units of time or units of samples, and if we want our digital system to remain constant with time independent of the sampling rate. Once our system is discrete we often prefer to work in (normalized) units of samples and not have to carry on the extra "step" term, in which case actual time will scale along with ...

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In the Total Variation framework we define 2 flavors: $$\text{Isotropic TV} \; {TV}_{ {L}_{2} } \left( X \right) = \sum_{ij} \sqrt{ { \left( {D}_{h} X \right) }_{ij}^{2} + { \left( {D}_{v} X \right) }_{ij}^{2} }$$  \text{Anisotropic TV} \; {TV}_{ {L}_{1} } \left( X \right) = \sum_{ij} \sqrt{ { \left( {D}_{h} X \right) }_{ij}^{2} } + \sqrt{{ \left( {D}_{...

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