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Following on from the above excellent answer, here is how to do it in python using scikit funcitons.
from skimage.feature import hessian_matrix, hessian_matrix_eigvals
#assume you have an image img
hxx, hxy, hyy = hessian_matrix(img, sigma=3)
i1, i2 = hessian_matrix_eigvals(hxx, hxy, hyy)
#i2 is the variable you want.
#Visualise the result
import ...
7
Be careful, a median filter cannot be expressed as a convolution, and thus is not considered a kernel in this respect. This is because the median filter is based on order statistics of an image patch, and the resulting pixel at the output of a median filter is not a linear combination of other pixels within a patch.
Otherwise, you are right, kernels are ...
7
It can be a little confusing at times, and the terms are not completely independent.
Detection:
In detection, you are simply detection the presence of something. For example, you might design an algorithm to detect beach-balls from pictures of a beach. You would feed an image into your algorithm, and it will spit out an answer 'yes', if there was a beach ...
7
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 ...
6
You need to work with different color space. Try YCbCr for instance
oimg = imread('http://i.stack.imgur.com/aYhrS.png');
img = rgb2ycbcr(oimg); % conver RGB to YCbCr color space
msk = img(:,:,3)>130; % apply threshold on Cr channel to get segmentation mask
I picked up 130 as arbitrary threshold, you might need to adjust it a bit.
Showing the input ...
6
I will try to give you some intuition into it by a different example.
Think we have 3 machines which can generate the numbers 1, 2, 3.
The first machine generates the number 1 with 80% and the numbers 2, 3 with 10% each.
The second machine generates the number 2 with 80% and the numbers 1, 3 with 10% each.
The third machine generates the number 3 with 80% ...
5
Other than commercial barcode reading algorithms (many of which fail to read challenging codes), I would like to direct you to this paper which is one of the best academic works in that field in my opinion:
Gallo, Manduchi: Reading Challenging Barcodes with Cameras.
Here is a more recent version:
Gallo, Manduchi: Reading 1D Barcodes with Mobile Phones ...
5
Ok,
First of all, pay attention it is a calculation per pixel using a sata from a blog.
Basically summing the Gradient norm over a block / windows.
To calculate what you submitted above do the following:
1.Define the Finite Differences filters to approximate the gradient. Moreover build the "Win Summation" kernel which sums all elements within a window.
...
5
This code work fine for me. You try
RGB = imread('Image/input.png');
GRAY = rgb2gray(RGB);
threshold = graythresh(GRAY);
originalImage = im2bw(GRAY, threshold);
originalImage = bwareaopen(originalImage,250);
se = strel('disk', 10); %# structuring element
closeBW = imclose(originalImage,se);
imshow(closeBW);
5
It can be done very easily with the scikit-learn. Examples are easy to find on their website, i.e. here. In my opinion it is the best way to go.
Modified code example from the above link:
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.datasets.samples_generator import make_blobs
########################...
5
1) Normalize your image to range $[0,255]$.
2) Select a threshold and threshold the image. For your image, what worked is: $\tau=[140-150]$.
3) Compute a Euclidean distance transform.
4) Apply watersheds segmentation.
If I apply this procedure, here is what I get:
Not perfect, but maybe a good start. The result looks similar to performing a Voronoi ...
5
We need to separate the concept of edge detection from the tools we use to apply the procedure.
Edges are local property of the image. Being so local means we don't analyze the image in frequency domain but in spatial domain.
Yet, a common step for edge detection is applying High Pass / Gradient Filter. Since those are Linear Shift Invariant operators we may ...
5
Well, I think the best way to tackle this question is a little background and a code as an example. I chose MATLAB for this example though PyTorch / Keras would probably be as easy.
This task requires the output to have the same dimensions as the input with per pixel classification (For other masks it could be regression as well).
A modern CNN architecture ...
4
Although you did not spent even a minute in researching the question I will post an answer to it. There are multiple ways; I will try to demonstrate it using wavelets in Mathematica.
So, first of all we need an image.
img = ExampleData[{"TestImage", "Mandrill"}]
Then we apply the DiscreteWaveletTransform using the HaarWavelet
dwd = ...
4
It depends on how you define the noise and what kind of noise does your image have. Different filters work on different kinds of noise. Among those filters, Wiener filter is often used by tailoring itself to the local image variance. And a new method called block-matching and 3D filtering (BM3D) in which the Weiner filter is used to optimize the parameters of ...
4
jpg is a compressed storage format and no matter how binary your image is, you will almost always end up with gray values in the resulting image (due to compression).
Please save it as png and try again.
(Also note that you still have 0s and 255s as the dominant bins, because jpg generally degrades around the edges, which constitute a small portion in the ...
4
A dynamic texture is a texture that is a function of space and time. In Image Synthesis papers, it is a term often used to designate things like:
a flame,
a waving flag,
specular reflections on water.
You can see some classic examples here.
4
You should use makecform to create a color transfer structure.
You should use srgb2lab to create a structure which will convert you to LAB.
To apply the color transformation use applycform.
Then, as a result you'll have an image in the LAB domain.
Then use kmeans to cluster the image colors on the AB channels.
Good Luck...
4
There are many properties of inhomogeneity:
Local Variance / STD.
Local Histogram.
The Gradient Function
Histogram of the Gradient.
Mean versus the Median / Mode.
4
You're trying to solve what's called Perona Malik Non Linear Diffusion Problem (Sometimes people call it, by mistake, Anisotropic Diffusion).
Anyhow, the simplest code for that is Anisotropic Diffusion (Perona & Malik) on The MATLAB File Exchange.
There is a more advanced (Anisotropic for real) algorithm in Fast Anisotropic Curvature Preserving ...
4
I think you have a matrix.
Each Row / Column is a descriptor vector of a point in the image.
Just like having features, let's say M features, and each point has M values corresponding to M features.
So each element in the descriptor vector is a value of specific feature for this point.
And yes, if you have M = 128 Features and 1000 points you'll get a ...
4
Thanks to Luminita Vese, who responded to this question via email. I will post the answer here. Let $\varphi_\epsilon(x) = \varphi(x) + \epsilon\eta(x)$ for some test function $\eta(x)$.
\begin{eqnarray}
\int_{\Omega} \frac{d}{d\epsilon} \left[ \delta(\varphi_\epsilon(x)) |\nabla (\varphi_\epsilon(x))|\right]dx &=& \int_{\Omega} \left[\frac d{d\...
4
These are two different concepts that you talk about. First, MRF gives you a framework to do discrete optimization of problems, which respect the Markovian property, that is a pixel is conditioned only on the neighboring ones (roughly stated). Typical applications include binary or multi-class labeling problems. Total variation on the other hand, is ...
4
One simple way to solve it is using Overlapping Patches.
Let's say you have image which is $ 20 \times 20 $ and you work on patches of the size $ 5 \times 5 $.
As I understand from your description you do 16 times denoising of $ 5 \times 5 $ patches.
What you should do is run the patches mask like in convolution. So each pixels (Ignoring boundaries) will ...
4
The approach seems reasonable.
Indeed doing edge detection in weighted RGB channel is the classic approach (Though you could also employ more advance methods, See Edge Detection on a Color Image).
I think you could achieve great results if you also look specifically for oval shapes then you reduce the chances for false positives.
Color identification in ...
4
Based on the blog post - The Paint Bucket in Paint.Net 4.0 (Video) I can tell it uses some edge detection to handle similar colors within a piece wise smooth area.
More information is given in the Paint Bucket Tool documentation.
Usually the way it can be implemented is by defining color metric. How far a color is form another color. If it within the ...
4
In case you can shoot a video of the static scene than a blinking light would be the easiest as you could easily detect it by subtracting the n - 1 frame from the n frame until you see something with high values.
If you take a still shot you can use 2 main ideas:
If the colors of the scene are from a given plate, find a color very different in Hue and make ...
4
The correct context of the refinement key word is segmentation.
Label Refinement in the context of image segmentation is a step to increase the resolution and understanding of the segmentation.
It can be done by exterior knowledge (Like labels on features) or other optimization steps to have a better results of the segmentation (Which basically labeling of ...
4
In general, the approach to take, is to have a local feature which has high value for such areas in the image.
There are many approaches to shape such a feature.
Probably the easiest one would be by local variance.
I tried 3 different approaches to this:
Local Variance by a Filter.
Local Variance of a Super Pixel.
Using the Weak Texture from Noise Level ...
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