8
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
I do not know about many segmentation techniques, but I have been dealing with structures that offer a "choice" of segmentation pieces that can be further examined to produce a satisfying segmentation.
Hopefully somebody else can write about some different state-of-the-art segmentation method that I don't know much about.
A small introduction as to why it ...
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
As an addendum to Penelope's answer, two popular families (and trendy) of algorithms.
Superpixels
A very popular family of algorithms called Superpixels is very trendy right now (there are even some Superpixel sessions in CV conferences). Superpixels are a lot like over-segmentation (like what watershed gives you), so some post-processing is required.
...
6
Alas, optical flow is a difficult problem too ;-)
Well, to be more constructive, here are a few algorithms that should be worth trying (or have been tried on this particular sequence) :
re-train your bags of features on a databse of vehicles more representative (in size and orientation) to your actual problem in order to obtain better results
use the fact ...
6
1st Approach:
Use the haartraining methods of opencv according to this tutorial http://note.sonots.com/SciSoftware/haartraining.html -- this should give the best results, but I haven't worked with haartraining myself so far...
2nd Approach:
I would suggest to use methods of "markerless tracking" of the individual tiles of the board. You can implement this ...
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 ...
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
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 ...
4
You could apply a 2d FFT. You will get a frequency spectrum as z values on a 2d plane:
← That's log(abs(fftshift(fft2(image))))) where image is from the question, but with white borders trimmed
From the z values you can determine the highest peak and calculate its position on the 2d plane. With some geometric calculation, you can derive wavelength, ...
4
I think the open-source command-line program potrace, might do what you want. It converts bitmaps to bezier curves and has a bunch of options allowing you to trade off smoothness and accuracy.
The open-source Inkscape vector (svg) editor has potrace built in (under the Path->Trace Bitmap menu option.) The result of applying your example in Inkscape, (I ...
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
Generally speaking, a change of color space is essentially a nonlinear transform that stretches and distorts the coordinates. The transform is also continuous, except maybe on a singularity line or half-plane. It does not preserve the distances, but preserves the neighborhoods.
In the case of RGB > Lab, you can see it as a linear transform (change of basis, ...
4
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% ...
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 ...
3
Another approach might be to perform the Radon / Hough transform first, then detect the points.
e.g. R = radon(I,0:179) in MATLAB.
It gives this image:
The x-axis is angle (0-180 deg) and the y-axis is distance from the centre. Each local minimum represents a line. It shows 6 lines ~75 degrees, 2 around 90 degrees, and 3 around 170 degrees. (This is ...
3
I guess for a global overview of the state of the art algorithms for segmentation one needs to look for the latest surveys. A good global overview with challenges are presented in Szeliski's Book.
3
Thought I should post my answer as it is bit different from other approaches. I tried this in Matlab.
sum all channels and create an image, so all channels are weighted equally
perform morphological closing and Gaussian filtering on this image
for each column of the resulting image, find the local maxima and construct an image
find the connected components ...
3
Here is yet an alternative solution to your problem by modelling your question as a 'path optimization problem'. Though it is more complicated than the simple binarization-and-then-curvefitting solution, it is more robust in practice.
From the very high level, we should consider this image as a graph, where
each image pixel is a node on this graph
each ...
3
This topic has always attracted a lot of interest, and yet no real consensus exists on the topic. Therefore I decided to drop a few words.
My answers to previously asked similar questions on stackexchange (Q1 and Q2) involved a subpixel curvilinear structure extraction algorithm by Steger. This method performed reasonably well in many cases and luckily, ...
3
Why not just use a simple 2D FFT (guassian) high pass filer?
I did this real quick using MATLAB
Shard #1 using high pass FFT:
The same thing is done on #2.
Shard #2 using high pass FFT:
As you can see, the background and glass area is wiped out, and only the edges are traced. I did not spend any time on it, but you can threshold the HP filtered output ...
3
I will describe my current approach, which is an combination of exploiting game rules, image processing and feature detection.
Relevant game rules
location tiles, castles and mountains can not be taken by game token (houses)
in 7 out of 8 cases water can also not be taken
there are 8 different (unique) kinds of sections, plus 8 through rotation by 180 ...
3
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.
...
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