I am a new to MATLAB. I have 6 histograms which are created from subdividing the image into patches. How I can merge these patches to make one histogram (i.e. concatenating the 6 histograms)?
I have tried this code:
subplot(3,4,1)
imhist(Patch1)
subplot(3,4,2)
imhist(Patch2)
subplot(3,4,3)
imhist(Patch3)
subplot(3,4,4)
imhist(Patch4)
You can concatenate all of the patches into a single vector, then call imhist on this vector. You mentioned that you have six histograms when your code is only showing four. As such, I will assume that the other histograms come from Patch5 and Patch6. Knowing this, the code would simply be:
patch = [Patch1(:); Patch2(:); Patch3(:); Patch4(:); Patch5(:); Patch6(:)];
imhist(patch);
Histograms are agnostic with the dimensions of the image you're looking at. It simply counts how many pixels you encounter per intensity level of the data you are using. As such, we can simply put all of these pixels for each patch into a single vector and do a histogram of that vector. This will work nicely as this will disregard the dimensions of each patch so we don't have to construct a new image and do a histogram of the newly constructed image.
Related
I have a data set that has two peaks close together. I'd like to fit these peaks with gaussians so that I come up with a new data set that replicates the original one. To this end, I am using MATLAB's "findpeaks" function, and using the heights and widths of the peaks in order to come up with the appropriate number of gaussians, and then add those gaussians together. However, because the peaks are so close together, the result looks like the following (with the original data set in blue and the replicated one in red):
Is there a better method to replicate the data with gaussian peaks?
Gaussian function are defined by two variable, mean and variance. The two peak would give you the means of the two gaussians and by the look of the figure the same variance for them both (If some data has gone through a Gaussian process the variance would be the same, I can not think of a physical process where that would not be the case, unless it is just an arbitrary plot). So you only have to find one variable. As for the peaks that would just be the normalization so that the area under the curve sums up to 1. A gaussian sums up to 1 by default, if the sum under the plot you are trying to fit is 2 you do not need to do anything, otherwise scale accordingly.
My guess is something like this (pseudo code):
f = 0.5*gauss(-3,var)+0.5*gauss(3,var)
If you know more about the process that created the plot, then you can actually do better.
I'm trying to transform my original gray image to mapped gray image using grey-scale mapping function. I have no idea how to get any two minima correspond to the grey-scale range [a,b] of the original histogram so that I can use these values for the equations below to get the mapped gray image.
f(x,y)=0 if [0,a),
f(x,y)=(255/(a-b))-a for [a,b],
f(x,y)=255 if (b,255]
Thank you!
So essentially you want to scale the histogram of your image to range from 0 - 255. All you need is the max and the min. The easiest way to find them is
a = min(I(:));
b = max(I(:));
Also I suspect you middle equation should actually be
f(x,y)=(255/(a-b))*(f(x,y)-a) for [a,b]
however that would eliminate the need for your first two equations. So it's possible that a and b are not the extrema in your case but that you are actually trying to accentuate some range of intensities that sit in the middle of your images histogram (and essentially discard all information outside of that range). In this case you have not given us enough information to suggest values for either a or b.
I am trying to implement a classification NN in Matlab.
My inputs are clusters of coordinates from an image. (Corresponding to delaunay triangulation vertexes)
There are 3 clusters (results of the optics algorithm) in this format:
( Not all clusters are of the same size.). Elements represent coordinates in euclidean 2d space . So (110,12) is a point in my image and the matrix depicted represents one cluster of points.
Clustering was done on image edges. So coordinates refer to logical values (always 1s in this case) on the image matrix.(After edge detection there are 3 "dense" areas in an image, and these collections of pixels are used for classification). There are 6 target classes.
So, my question is how can I format them into single column vector inputs to use in a neural network?
(There is a relevant answer here but I would like some elaboration if possible. ( I am probably too tired right now from 12 hours of trying stuff and dont get it 100% :D :( )
Remember, there are 3 different coordinate matrices for each picture, so my initial thought was, create an nn with 3 inputs (of different length). But how to serialize this?
Here's a cluster with its tags on in case it helps:
For you to train the classifier, you need a matrix X where each row will correspond to an image. If you want to use a coordinate representation, this means all images will have to be of the same size, say, M by N. So, the row of an image will have M times N elements (features) and the corresponding feature values will be the cluster assignments. Class vector y will be whatever labels you have, that is one of the six different classes you mentioned through the comments above. You should keep in mind that if you use a coordinate representation, X can get very high-dimensional, and unless you have a large number of images, chances are your classifier will perform very poorly. If you have few images, consider using fractions of pixels belonging to clusters that I suggested in one of the comments: this can give you a shorter feature description that is invariant to rotation and translation, and may yield better classification.
I have about 10000 floating point data, and have read them into a single row matrix.
Now I would like to plot them and show their distribution, would there be some simple functions to do that?
plot() actually plots value with respect to data number...which is not what I want
bar() is similar to what I want, but actually I would like to lower the sample rate and merge neighbor bars which are close enough (e.g. one bar for 0.50-0.55, and one bar for 0.55-0.60, etc) instead of having one single bar for every single data sample.
would there be a function to calculate this distribution by dividing the range into small steps, and outputting the prob density in each step?
Thank you!
hist() would be best. It plots a histogram, with a lot of options which you can see by doc hist, or by checking the Matlab website. Options include a specified number of bins, or a range of bins. This will plot a histogram of 1000 normally random points, with 50 bins.
hist(randn(1000,1),50)
Say, I have a cube of dimensions 1x1x1 spanning between coordinates (0,0,0) and (1,1,1). I want to generate a random set of points (assume 10 points) within this cube which are somewhat uniformly distributed (i.e. within certain minimum and maximum distance from each other and also not too close to the boundaries). How do I go about this without using loops? If this is not possible using vector/matrix operations then the solution with loops will also do.
Let me provide some more background details about my problem (This will help in terms of what I exactly need and why). I want to integrate a function, F(x,y,z), inside a polyhedron. I want to do it numerically as follows:
$F(x,y,z) = \sum_{i} F(x_i,y_i,z_i) \times V_i(x_i,y_i,z_i)$
Here, $F(x_i,y_i,z_i)$ is the value of function at point $(x_i,y_i,z_i)$ and $V_i$ is the weight. So to calculate the integral accurately, I need to identify set of random points which are not too close to each other or not too far from each other (Sorry but I myself don't know what this range is. I will be able to figure this out using parametric study only after I have a working code). Also, I need to do this for a 3D mesh which has multiple polyhedrons, hence I want to avoid loops to speed things out.
Check out this nice random vectors generator with fixed sum FEX file.
The code "generates m random n-element column vectors of values, [x1;x2;...;xn], each with a fixed sum, s, and subject to a restriction a<=xi<=b. The vectors are randomly and uniformly distributed in the n-1 dimensional space of solutions. This is accomplished by decomposing that space into a number of different types of simplexes (the many-dimensional generalizations of line segments, triangles, and tetrahedra.) The 'rand' function is used to distribute vectors within each simplex uniformly, and further calls on 'rand' serve to select different types of simplexes with probabilities proportional to their respective n-1 dimensional volumes. This algorithm does not perform any rejection of solutions - all are generated so as to already fit within the prescribed hypercube."
Use i=rand(3,10) where each column corresponds to one point, and each row corresponds to the coordinate in one axis (x,y,z)