# Fitting a two-dimensional Gaussian to the image I have a image which I want to fit 2d Gaussian function with that. I used the below codes but the output figure doesn't look alright. I extracted the mean and covariance values in x and y direction.I made my Gaussian function by using mean and covariance and using multinormaldistribution, However my out put figures doesn't fit with my data. my original data is txt file image size is 50 to 50 pixels. https://pastebin.com/NF0j3wk1


image = ReadList["color-2.txt", Number, RecordLists -> True];
image2 = ListPlot3D[image, PlotRange -> All];
{m, n} = Dimensions[image]

min = Min[image]
sum = Total[image - min, Infinity];
p = (image - min)/sum;

ListPlot3D[p, PlotRange -> All]

Dimensions[p]
mean = Sum[{x, y} p[[x, y]], {x, 1, n}, {y, 1, m}]

cov = Sum[ Outer[Times, {x, y} - mean, {x, y} - mean] p[[x, y]],
{x, 1, n}, {y, 1, m}]

f[x_, y_] := PDF[MultinormalDistribution[mean, cov], {x,y}] // Evaluate

g[x_, y_] := (f[x, y] sum + min) 4;

image3 = Plot3D[g[x, y], {x, 1, 50}, {y, 1, 50}, MeshStyle -> None,
PlotStyle -> Opacity[0.8], PlotRange -> All]

Show[ListContourPlot[image, Contours -> 10, ContourShading -> None, ContourStyle -> ColorData[1, 1], InterpolationOrder -> 3],
ContourPlot[g[x, y], {x, 1, 50}, {y, 1, 50}, Contours -> 10,
ContourShading -> None, ContourStyle -> ColorData[1, 2]]]


• Hello and welcome to Mathematica Stackexchange! Can you please edit your question and use the code tags  to highlight your code? Otherwise it's pretty unreadable. – infinitezero Sep 28 at 11:42
• You should attach the image, or put color-2.txt on pastebin otherwise it's harder to see where your fitting is going wrong. Note you can use Rescale to adjust the image first instead of doing this manually with Min and Total. – flinty Sep 28 at 12:45
• I think your covariance calculation is wrong. It should be cov = Sum[ Outer[Times, {x, y} - mean, {x, y} - mean] p[[y, x]], {y, 1, m}, {x,1, n}] You are thinking in terms of x,y spatial positions, but the image data are indexed by {row, column}. After using this fix, the resulting plots look like they have the correct orientation. – flinty Sep 28 at 12:53
• @infinitezero I changed the code – mrnr Sep 28 at 14:58
• @flinty I changed the dimension at cov section, however still I can not fit it properly – mrnr Sep 28 at 15:00

I don't think there's anything wrong with your code or Mathematica. But you're not getting a good fit because the tails of the distribution are much heavier than a bivariate normal distribution.

What happens is that the heavy tails result in large estimates of the variances (or equivalently, the standard deviations). When plotting the resulting bivariate normal distribution you get a much more spread out surface than that from the original "image" (which is what your image seems to show). It is the assumption of a bivariate normal distribution that is incorrect.

One can also show this by estimating the values of kurtosis for both dimensions. If the shape is a bivariate normal, then one should get a value close to 3.

data = Flatten[Table[{x, y, p[[x, y]]}, {x, 50}, {y, 50}], 1]
wx = WeightedData[data[[All, 1]], data[[All, 3]]]
wy = WeightedData[data[[All, 2]], data[[All, 3]]]

Kurtosis[wx]
(* 4.03682 *)

Kurtosis[wy]
(* 5.22927 *)


Those values are much larger than 3 which strongly suggests the the underlying bivariate distribution is not a bivariate normal (as the marginal distributions are not normal).

So maybe you're not yet convinced that the bivariate distribution is not a bivariate normal. If that distribution were bivariate normal, then the marginal distributions would be normal. But one can show (maybe more convincing that giving the kurtosis values) the marginal densities are not normal.

(Note that this repeats some of the code above.)

data = Flatten[Table[{x, y, p[[x, y]]}, {x, 50}, {y, 50}], 1]
xx = ConstantArray[{0, 0}, 50];
yy = ConstantArray[{0, 0}, 50];
Do[xx[[i]] = {i, Total[Select[data, #[] == i &][[All, 3]]]};
yy[[i]] = {i, Total[Select[data, #[] == i &][[All, 3]]]}, {i, 50}]
wxx = WeightedData[xx[[All, 1]], xx[[All, 2]]];
wyy = WeightedData[yy[[All, 1]], yy[[All, 2]]];

Show[ListPlot[xx],
Plot[PDF[NormalDistribution[Mean[wxx], StandardDeviation[wxx]], x], {x, 1, 50}]] Show[ListPlot[yy],
Plot[PDF[NormalDistribution[Mean[wyy], StandardDeviation[wyy]], x], {x, 1, 50}]]
` These are the original marginal distributions and the normal distributions with the same mean and variance. The marginal distributions are clearly not normal. The original marginal distributions have very heavy tails (unlike a normal) as you don't see them going to zero and they actually seem like they've leveled off at some positive value.

It would help if you described how you got the original surface as a bivariate normal is not an adequate fit. For example, is the surface a bivariate probability density function estimated from a random sample? Or is the surface from a set of measurements at the grid points and you think the surface has the same "shape" as a bivariate normal distribution (i.e., same shape but no probabilistic interpretation)?

• Hi my Kurtosis for both values for both dimensions are about 4.03 which is larger that 3. But I am getting confused so how could I normalise them properly? – mrnr Sep 29 at 0:43
• What you've done is displayed a bivariate normal with the same means, variances, and correlation coefficient as the original surface. That's all correct. However, that surface is not the same as a bivariate normal so you see a lack of fit. So maybe the information you need to add is how you got that original surface in the first place. Was it from a random sample from a bivariate distribution? Some theoretical construct that you think should be a bivariate normal? – JimB Sep 29 at 3:45