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I have a set of points W={(x1, y1), (x2, y2),..., (xn, yn)} on the 2D plane. Can you find an algorithm that takes these points as the input and returns a point (x, y) on the 2D plane which has the minimum sum of distances from the points in W? In other words, if
di = Euclidean_distance((x, y), (xi, yi))
I want to minimize:
d1 + d2 + ... + dn
The Problem
You're looking for the geometric median.
An Easy Solution
There is no closed-form solution to this problem, so iterative or probabilistic methods are used. The easiest way to find this is probably with Weiszfeld's algorithm:
We can implement this in Python as follows:
import numpy as np
from numpy.linalg import norm as npnorm
c_pt_old = np.random.rand(2)
c_pt_new = np.array([0,0])
while npnorm(c_pt_old-c_pt_new)>1e-6:
num = 0
denom = 0
for i in range(POINT_NUM):
dist = npnorm(c_pt_new-pts[i,:])
num += pts[i,:]/dist
denom += 1/dist
c_pt_old = c_pt_new
c_pt_new = num/denom
print(c_pt_new)
There's a chance that Weiszfeld's algorithm won't converge, so it might be best to run it several times from different starting points.
A General Solution
You can also find this using second-order cone programming (SOCP). In addition to solving your specific problem, this general formulation then allows you to easily add constraints and weightings, such as variable uncertainty in the location of each data point.
To do so, you create a number of indicator variables representing the distance between the proposed center point and the data points.
You then minimize the sum of the indicator variables. The result follows
import cvxpy as cp
import numpy as np
import matplotlib.pyplot as plt
#Generate random test data
POINT_NUM = 100
pts = np.random.rand(POINT_NUM,2)
c_pt = cp.Variable(2) #The center point we wish to locate
distances = cp.Variable(POINT_NUM) #Distance from the center point to each data point
#Generate constraints. These are used to hold distances.
constraints = []
for i in range(POINT_NUM):
constraints.append( cp.norm(c_pt-pts[i,:])<=distances[i] )
objective = cp.Minimize(cp.sum(distances))
problem = cp.Problem(objective,constraints)
optimal_value = problem.solve()
print("Optimal value = {0}".format(optimal_value))
print("Optimal location = {0}".format(c_pt.value))
plt.scatter(x=pts[:,0], y=pts[:,1], s=1)
plt.scatter(c_pt.value[0], c_pt.value[1], s=10)
plt.show()
SOCPs are available in a number of solvers including CPLEX, Elemental, ECOS, ECOS_BB, GUROBI, MOSEK, CVXOPT, and SCS.
I've tested and the two approaches give the same answers to within tolerance.
Weiszfeld, E. (1937). "Sur le point pour lequel la somme des distances de n points donnes est minimum". Tohoku Mathematical Journal. 43: 355–386.
If that point does not need to be from your sample, then the mean minimises the euclidean distance.
A third method would be to use a compact nonlinear programming formulation. An unconstrained NLP model would be:
min sum(i, ||x-p(i)|| )
This has just 2 variables (the coordinates of x).
There is a very good initial point available. Let p(i,c) be the coordinates of the data points. Then the mean is
m(c) = sum(i, p(i,c)) / n
where n is the number of data points. This point is often very close to the optimal value of x. So we can use m as an excellent initial point for x.
Some limited experiments indicate this approach is quite faster than a cone programming formulation for large n.
For details see Yet Another Math Programming Consultant - Finding the Central Point in a Point Cloud blog post.
I need to implement a high pass Butterworth filter in MATLAB for the purposes of image filtering. I have implemented one but it looks like it doesn't work. Here is the code I have written. Can anyone tell me what is wrong?
n=1;
d=50;
A=1.5;
im=imread('imagex.jpg');
h=size(im,1);
w=size(im,2);
[x y]=meshgrid(-floor(w/2):floor(w-1/2),-floor(h/2):floor(h-1/2));
hhp=(1./(d./(x.^2+y.^2).^0.5).^(2*n));
image_2Dfilter=fftshift(fft2(im));
Image_butterworth=image_2Dfilter;
imshow(Image_butterworth);
ifftshow(Image_butterworth);
For one thing, there is no such command called ifftshow. Secondly, you aren't filtering anything. All you're doing is visualizing the spectrum of the image.
In terms of visualizing the spectrum, how you're doing it right now is very dangerous. For one thing, you are visualizing the coefficients at each spatial frequency component which is complex-valued in nature. If you want to visualize the spectrum in a way that makes sense to most of us, it's better to take a look at either the magnitude or phase. However, because this is a Butterworth filter, it's best to apply it to the magnitude of the filter.
You can find the magnitude of the spectrum by using the abs function. Even when you do that, if you did imshow directly on the magnitude, you will get a visualization that is zero everywhere except for the middle. This is because the DC component is so large and the rest of the spectrum is small in comparison.
Let me show you an example. This is the cameraman image that is part of the image processing toolbox:
im = imread('cameraman.tif');
figure;
imshow(im);
Now, let's visualize the spectrum and ensuring that the DC component is in the centre of the image - you already did this with fftshift. It's also a good idea to cast the image to double to ensure the best precision of data. In addition, make sure you apply abs to find the magnitude:
fftim = fftshift(fft2(double(im)));
mag = abs(fftim);
figure;
imshow(mag, []);
As you can see, it's not very useful due to the reason that I mentioned. A better way to visualize the spectrum of the image is usually to apply a log transformation to the spectrum. This is also useful if you want to de-mean or remove the mean so that the dynamic range fits better for display. In other words, you would add 1 to the magnitude, then apply a logarithm to the magnitude so that higher values can taper off. It doesn't matter which base you use, so I'll just use the natural logarithm which is encapsulated by the log command:
figure;
imshow(log(1 + mag), []);
Now that's much better. Now we'll get onto your filtering mechanism. Your Butterworth filter is slightly incorrect. The meshgrid of coordinates is slightly wrong. The -1 operation that's at the ending interval needs to go outside:
[x y]=meshgrid(-floor(w/2):floor(w/2)-1,-floor(h/2):floor(h/2)-1);
Remember, you are defining a symmetric interval about the centre of the image, and what you had originally wasn't correct. I'd also like to mention that this looks like a high-pass filter, so the output should look like an edge detection. In addition, the definition of the Butterworth high pass filter is incorrect. The correct definition of the filter in frequency domain is:
D(u,v) is the distance from the centre of the image in frequency domain, Do is the cutoff distance while B is a controlling scale factor controlling what the desired gain would be at the cutoff distance. n is the order of the filter. Do in your case is d = 50. In practice, B = sqrt(2) - 1 so that at the cutoff distance of Do, D(u,v) = 1 / sqrt(2) = 0.707, which is the 3 dB cutoff frequency mostly seen in electronics circuit filters. Sometimes you'll see B being set to 1 for simplicity, but it's common to set this to B = sqrt(2) - 1.
However, your current code isn't doing any filtering. To filter in the frequency domain, you simply multiply the spectrum of the image with the spectrum of the filter itself. This is equivalent to convolution in the spatial domain. Once you do that, you simply undo the fftshift that was performed on the image, take the inverse FFT and then eliminate any imaginary components that are due to numerical imprecision. Also, let's cast to uint8 to make sure that we respect the original image type.
That can be done like so:
%// Your code with meshgrid fix
n=1;
d=50;
h=size(im,1);
w=size(im,2);
fftim = fftshift(fft2(double(im)));
[x y]=meshgrid(-floor(w/2):floor(w/2)-1,-floor(h/2):floor(h/2)-1);
%hhp=(1./(d./(x.^2+y.^2).^0.5).^(2*n));
%%%%%%// New code
B = sqrt(2) - 1; %// Define B
D = sqrt(x.^2 + y.^2); %// Define distance to centre
hhp = 1 ./ (1 + B * ((d ./ D).^(2 * n)));
out_spec_centre = fftim .* hhp;
%// Uncentre spectrum
out_spec = ifftshift(out_spec_centre);
%// Inverse FFT, get real components, and cast
out = uint8(real(ifft2(out_spec)));
%// Show image
imshow(out);
If you want to see what the filtered spectrum looks like, just do this:
figure;
imshow(log(1 + abs(out_spec_centre)), []);
We get:
This makes sense. You see that in the middle of the spectrum, it's slightly darker in comparison to the outer edges of the spectrum. That's because with the high-pass Butterworth filter, you are amplifying the higher frequency terms and it gets visualized to be a higher intensity.
Now, out contains your filtered image, and we finally get this:
That looks like a fine result! However, naively casting the image to uint8 truncates any negative values to 0 and any positive values greater than 255 to 255. Because this is an edge detection, you want to detect both the negative and positive transitions... so a good idea would be to normalize the output so that it ranges from [0,1], and then cast with uint8 after you multiply by 255. This way, no changes in the image get visualized to gray, negative changes get visualized as dark and positive changes get visualized as white.... so you'd do something like this:
%// Your code with meshgrid fix
n=1;
d=50;
h=size(im,1);
w=size(im,2);
fftim = fftshift(fft2(double(im)));
[x y]=meshgrid(-floor(w/2):floor(w/2)-1,-floor(h/2):floor(h/2)-1);
%hhp=(1./(d./(x.^2+y.^2).^0.5).^(2*n));
%%%%%%// New code
B = sqrt(2) - 1; %// Define B
D = sqrt(x.^2 + y.^2); %// Define distance to centre
hhp = 1 ./ (1 + B * ((d ./ D).^(2 * n)));
out_spec_centre = fftim .* hhp;
%// Uncentre spectrum
out_spec = ifftshift(out_spec_centre);
%// Inverse FFT, get real components
out = real(ifft2(out_spec));
%// Normalize and cast
out = (out - min(out(:))) / (max(out(:)) - min(out(:)));
out = uint8(255*out);
%// Show image
imshow(out);
We get this:
I think that you should work a little bit diferent
n=1;
D0=50; % change the name for d0, d is usuaally the (u²+v²)⁽1/2)
A=1.5; % normally the amplitude is 1
im=imread('cameraman.jpg');
[M,N]=size(im); % is easy to get the h and w like this
% compute the 2d fourier transform in order to multiply
F=fft2(double(im));
% compute your filter and do the meshgrid for your matrix but it is M*n, and get only the real part
u=0:(M-1);
v=0:(N-1);
idx=find(u>M/2);
u(idx)=u(idx)-M;
idy=find(v>N/2);
v(idy)=v(idy)-N;
[V,U]=meshgrid(v,u);
D=sqrt(U.^2+V.^2);
H =A * (1./(1 + (D0./D).^(2*n)));
% multiply element by element
G=H.*F;
g=real(ifft2(double(G)));
subplot(1,2,1); imshow(im); title('Input image');
subplot(1,2,2); imshow(g,[ ]); title('filtered image');
I want to calculate the Euclidean distance between two images in Matlab. I find some examples and I've try them but they are not correct.
The result of this Euclidean distance should be between 0 and 1 but with two different ways I reached to different solutions.
The first algorithm gives me a 4 digit number such as 2000 and other digits like this and by the other way I reached numbers such as 0.007
What is wrong with it?
This is one of those algorithms I mentioned:
Im1 = imread('1.jpeg');
Im2 = imread('2.jpeg');
Im1 = rgb2gray(Im1);
Im2 = rgb2gray(Im2);
hn1 = imhist(Im1)./numel(Im1);
hn2 = imhist(Im2)./numel(Im2);
% Calculate the Euclidean distance
f = sum((hn1 - hn2).^2)
the final line of code needs a sqrt command:
f = sum(sqrt(hn1-hn2).^2);
check this link
You can also use the norm command
f = norm(hn1-hn2);
These post1 and post2 can be useful.
Oh, I'm not sure where to begin but here are some things that you should think about:
1: You're normalising your histograms incorrectly. You want them to have unit L1-norm:
hn1 = imhist(Im1);
hn2 = imhist(Im2);
hn1 = hn1/numel(hn1);
hn2 = hn2/numel(hn2);
2: Taking L2-distance between histograms doesn't really make sense (what is an euclidian distance between two distributions really?). You should rather take a look at something like a L1 or Chi-2 distance, or use an intersection kernel. L1 would be
f=norm(hn1-hn2,1);
3: If you really do want it to be L2 euclidian distance, the last line should be
f=norm(hn1-hn2);
but then you should rather L2-normalize the histogram:
hn1 = imhist(Im1);
hn2 = imhist(Im2);
hn1 = hn1/norm(hn1);
hn2 = hn2/norm(hn2);
4: Please try to be clearer in the formulation of your questions - it was a bit hard to decode :). If your would have mentioned the application - I could have given some additional pointers. :)
Given a set of points, what's the fastest way to fit a parabola to them? Is it doing the least squares calculation or is there an iterative way?
Thanks
Edit:
I think gradient descent is the way to go. The least squares calculation would have been a little bit more taxing (having to do qr decomposition or something to keep things stable).
If the points have no error associated, you may interpolate by three points. Otherwise least squares or any equivalent formulation is the way to go.
I recently needed to find a parabola that passes through 3 points.
suppose you have (x1,y1), (x2,y2) and (x3,y3) and you want the parabola
y-y0 = a*(x-x0)^2
to pass through them: find y0, x0, and a.
You can do some algebra and get this solution (providing the points aren't all on a line) :
let c = (y1-y2) / (y2-y3)
x0 = ( -x1^2 + x2^2 + c*( x2^2 - x3^2 ) ) / (2.0*( -x1+x2 + c*x2 - c*x3 ))
a = (y1-y2) / ( (x1-x0)^2 - (x2-x0)^2 )
y0 = y1 - a*(x1-x0)^2
Note in the equation for c if y2==y3 then you've got a problem. So in my algorithm I check for this and swap say x1, y1 with x2, y2 and then proceed.
hope that helps!
Paul Probert
A calculated solution is almost always faster than an iterative solution. The "exception" would be for low iteration counts and complex calculations.
I would use the least squares method. I've only every coded it for linear regression fits but it can be used for parabolas (I had reason to look it up recently - sources included an old edition of "Numerical Recipes" Press et al; and "Engineering Mathematics" Kreyzig).
ALGORITHM FOR PARABOLA
Read no. of data points n and order of polynomial Mp .
Read data values .
If n< Mp
[ Regression is not possible ]
stop
else
continue ;
Set M=Mp + 1 ;
Compute co-efficient of C-matrix .
Compute co-efficient of B-matrix .
Solve for the co-efficients
a1,a2,. . . . . . . an .
Write the co-efficient .
Estimate the function value at the glren of independents variables .
Using the free arbitrary accuracy math program "PARI" (for Mac or PC):
Here is how I would fit a parabola to a set of 641 points,
and I also show how to find the minimum of that parabola:
Set a high number of digits of precision:
\p 300
Write the data points to a text file separated by one space
for each data point
(use ASCII characters in base ten, no space at file start or file end, and no returns, write extremely large or small floating points as for example
"9.0E-23" but not "9.0D-23" ).
make a string to point to that file:
fileone="./desktop/data.txt"
read that file into PARI using the following instructions:
fileopen(fileone,r)
readsplit(file) = my(cmd);cmd="perl -ne \"chomp; print '[' . join(',', split(/ +/)) . ']\n';\"";eval(externstr(Str(cmd," ",file)))
readsplit(fileone)
Label that data with a name:
in = %
V = in[1]
Define a least squares fit function:
lsf(X,Y,n) = my(M=matrix(#X,n+1,i,j,X[i]^(j-1)));fit=Polrev(matsolve(M~*M,M~*Y~))
Apply that lsf function to your 641 data points:
lsf([-320..320],V, 2)
Then if you want to show the minimum of that parabolic fit, enter:
xextreme = solve (x=-1000,1000,eval(deriv(fit)));print (xextreme*(124.5678-123.5678)/640+(124.5678+123.5678)/2);x=xextreme;print(eval(fit))
(I had to adjust for my particular x-axis scaling before the "print" statement in that command line above).
(Note: A sacrifice made to simplify this algorithm
causes it to work only
when the data set has equally spaced x-axis coordinates.)
I was worried that my last post
was too compact to follow and
too hard to convert to other environments.
I would like to show here how to solve the
generalized problem of parabolic data fitting explicitly
without specialized matrix math terminology;
and so that each multiplication, division,
subtraction and addition can be seen at once.
To save ink this fit reparameterizes the x-axis as evenly
spaced points centered on zero
so that odd powered sums all get eliminated
(saving a lot of space and time),
so the x-coordinates of the N data points
are effectively labeled by points
of this vector: X=[-(N-1)/2..(N-1)/2].
For example "xextreme" will be returned
versus those integer indices
and so (if desired) a simple (consumes very little CPU time)
linear transformation must be applied after the algorithm below
to get it versus your problem's particular x-axis labels.
This is written in the language of
the free program "PARI" but all the
commands are simple to translate to any language.
Step 1: assign a label to the y-axis data:
? V=[5,2,1,2,5]
"PARI" confirms that entry:
%280 = [5, 2, 1, 2, 5]
Then type in the following processing algorithm
which calculates a best fit parabola
through any y-axis data set with constant x-axis separation:
? g=#V;h=(g-1)*g*(g+1)/3;i=h*(3*g*g-7)/5;\
a=sum(i=1,g,V[i]);b=sum(i=1,g,(2*i-1-g)*V[i]);c=sum(i=1,g,(2*i-1-g)*(2*i-1-g)*V[i]);\
A=matdet([a,c;h,i])/matdet([g,h;h,i]);B=b/h*2;C=matdet([g,h;a,c])/matdet([g,h;h,i])*4;\
xextreme=-B/(2*C);yextreme=-B*B/(4*C)+A;fit=Polrev([A,B,C]);\
print("\n","y of extreme is ",yextreme,"\n","which occurs this many data points from center of data: ",xextreme)
(Note for non-PARI users:
the command "matdet([a,c;h,i])"
is just another way of entering "a*i-c*h")
Those commands then produce the following screen output:
y of extreme is 1
which occurs this many data points from center of data: 0
The algorithm stores the polynomial of the fit in the variable "fit":
? fit
%282 = x^2 + 1
?
(Note that to make that algorithm short
the x-axis labels are assigned as X=[-(N-1)/2..(N-1)/2],
thus they are X=[-2,-1,0,1,2]
To correct that
for the same polynomial as parameterized
by an x-axis coordinate data set of say X=[−1,0,1,2,3]:
just apply a simple linear transform, in this case:
"x^2 + 1" --> "(t - 1)^2 + 1".)
I'm using procedural techniques to generate graphics for a game I am writing.
To generate some woods I would like to scatter trees randomly within a regular hexagonal area centred at <0,0>.
What is the best way to generate these points in a uniform way?
If you can find a good rectangular bounding box for your hexagon, the easiest way to generate uniformly random points is by rejection sampling (http://en.wikipedia.org/wiki/Rejection_sampling)
That is, find a rectangle that entirely contains your hexagon, and then generate uniformly random points within the rectangle (this is easy, just independently generate random values for each coordinate in the right range). Check if the random point falls within the hexagon. If yes, keep it. If no, draw another point.
So long as you can find a good bounding box (the area of the rectangle should not be more than a constant factor larger than the area of the hexagon it encloses), this will be extremely fast.
A possibly simple way is the following:
F ____ B
/\ /\
A /__\/__\ E
\ /\ /
\/__\/
D C
Consider the parallelograms ADCO (center is O) and AOBF.
Any point in this can be written as a linear combination of two vectors AO and AF.
An point P in those two parallelograms satisfies
P = x* AO + y * AF or xAO + yAD.
where 0 <= x < 1 and 0 <= y <= 1 (we discount the edges shared with BECO).
Similarly any point Q in the parallelogram BECO can be written as the linear combination of vectors BO and BE such that
Q = xBO + yBE where 0 <=x <=1 and 0 <=y <= 1.
Thus to select a random point
we select
A with probability 2/3 and B with probability 1/3.
If you selected A, select x in [0,1) (note, half-open interval [0,1)) and y in [-1,1] and choose point P = xAO+yAF if y > 0 else choose P = x*AO + |y|*AD.
If you selected B, select x in [0,1] and y in [0,1] and choose point Q = xBO + yBE.
So it will take three random number calls to select one point, which might be good enough, depending on your situation.
If it's a regular hexagon, the simplest method that comes to mind is to divide it into three rhombuses. That way (a) they have the same area, and (b) you can pick a random point in any one rhombus with two random variables from 0 to 1. Here is a Python code that works.
from math import sqrt
from random import randrange, random
from matplotlib import pyplot
vectors = [(-1.,0),(.5,sqrt(3.)/2.),(.5,-sqrt(3.)/2.)]
def randinunithex():
x = randrange(3);
(v1,v2) = (vectors[x], vectors[(x+1)%3])
(x,y) = (random(),random())
return (x*v1[0]+y*v2[0],x*v1[1]+y*v2[1])
for n in xrange(500):
v = randinunithex()
pyplot.plot([v[0]],[v[1]],'ro')
pyplot.show()
A couple of people in the discussion raised the question of uniformly sampling a discrete version of the hexagon. The most natural discretization is with a triangular lattice, and there is a version of the above solution that still works. You can trim the rhombuses a little bit so that they each contain the same number of points. They only miss the origin, which has to be allowed separately as a special case. Here is a code for that:
from math import sqrt
from random import randrange, random
from matplotlib import pyplot
size = 10
vectors = [(-1.,0),(.5,sqrt(3.)/2.),(.5,-sqrt(3.)/2.)]
def randinunithex():
if not randrange(3*size*size+1): return (0,0)
t = randrange(3);
(v1,v2) = (vectors[t], vectors[(t+1)%3])
(x,y) = (randrange(0,size),randrange(1,size))
return (x*v1[0]+y*v2[0],x*v1[1]+y*v2[1])
# Plot 500 random points in the hexagon
for n in xrange(500):
v = randinunithex()
pyplot.plot([v[0]],[v[1]],'ro')
# Show the trimmed rhombuses
for t in xrange(3):
(v1,v2) = (vectors[t], vectors[(t+1)%3])
corners = [(0,1),(0,size-1),(size-1,size-1),(size-1,1),(0,1)]
corners = [(x*v1[0]+y*v2[0],x*v1[1]+y*v2[1]) for (x,y) in corners]
pyplot.plot([x for (x,y) in corners],[y for (x,y) in corners],'b')
pyplot.show()
And here is a picture.
alt text http://www.freeimagehosting.net/uploads/0f80ad5d9a.png
The traditional approach (applicable to regions of any polygonal shape) is to perform trapezoidal decomposition of your original hexagon. Once that is done, you can select your random points through the following two-step process:
1) Select a random trapezoid from the decomposition. Each trapezoid is selected with probability proportional to its area.
2) Select a random point uniformly in the trapezoid chosen on step 1.
You can use triangulation instead of trapezoidal decomposition, if you prefer to do so.
Chop it up into six triangles (hence this applies to any regular polygon), randomly choose one triangle, and randomly choose a point in the selected triangle.
Choosing random points in a triangle is a well-documented problem.
And of course, this is quite fast and you'll only have to generate 3 random numbers per point --- no rejection, etc.
Update:
Since you will have to generate two random numbers, this is how you do it:
R = random(); //Generate a random number called R between 0-1
S = random(); //Generate a random number called S between 0-1
if(R + S >=1)
{
R = 1 – R;
S = 1 – S;
}
You may check my 2009 paper, where I derived an "exact" approach to generate "random points" inside different lattice shapes: "hexagonal", "rhombus", and "triangular". As far as I know it is the "most optimized approach" because for every 2D position you only need two random samples. Other works derived earlier require 3 samples for each 2D position!
Hope this answers the question!
http://arxiv.org/abs/1306.0162
1) make biection from points to numbers (just enumerate them), get random number -> get point.
Another solution.
2) if N - length of hexagon's side, get 3 random numbers from [1..N], start from some corner and move 3 times with this numbers for 3 directions.
The rejection sampling solution above is intuitive and simple, but uses a rectangle, and (presumably) euclidean, X/Y coordinates. You could make this slightly more efficient (though still suboptimal) by using a circle with radius r, and generate random points using polar coordinates from the center instead, where distance would be rand()*r, and theta (in radians) would be rand()*2*PI.