I have some discrete values and assumption, that these values lie on a Gaussian curve.
There should be an algorithm for max-calculation using only 3 discrete values.
Do you know any library or code in C/C++ implementing this calculation?
Thank you!
P.S.:
The original task is auto-focus implementation. I move a (microscope) camera and capture the pictures in different positions. The position having most different colors should have best focus.
EDIT
This was long time ago :-(
I'just wanted to remove this question, but left it respecting the good answer.
You have three points that are supposed to be on a Gaussian curve; this means that they lie on the function:
If you take the logarithm of this function, you get:
which is just a simple 2nd grade polynomial, i.e. a parabola with a vertical axis of simmetry:
with
So, if you know the three coefficients of the parabola, you can derive the parameters of the Gaussian curve; incidentally, the only parameter of the Gaussian function that is of some interest to you is b, since it tells you where the center of the distribution, i.e. where is its maximum. It's immediate to find out that
All that remains to do is to fit the parabola (with the "original" x and the logarithm of your values). Now, if you had more points, a polynomial fit would be involved, but, since you have just three points, the situation is really simple: there's one and only one parabola that goes through three points.
You now just have to write the equation of the parabola for each of your points and solve the system:
(with , where the zs are the actual values read at the corresponding x)
This can be solved by hand (with some time), with some CAS or... looking on StackOverflow :) ; the solution thus is:
So using these last equations (remember: the ys are the logarithm of your "real" values) and the other relations you can easily write a simple algebraic formula to get the parameter b of your Gaussian curve, i.e. its maximum.
(I may have done some mess in the calculations, double-check them before using the results, anyhow the procedure should be correct)
(thanks at http://www.codecogs.com/latex/eqneditor.php for the LaTeX equations)
Related
I'm trying to do triangulation for 3D reconstruction and I came across an interesting observation which I cannot justify.
I have 2 sets of images. I know the correspondences and I'm finding the intrinsic and extrinsic parameters using a direct linear transformation. While I'm able to properly reconstruct the original scene, the intrinsic parameters are different even though the pictures are taken from the same camera. How is it possible to have different intrinsic parameters if the camera is the same? Also, if the intrinsic parameters are different, how am I able to reconstruct the scene perfectly?
Thank you
You haven't specified what you mean by "different", so i'm just going to point two possible sources of differences that come to mind. Let's denote the matrix of intrinsic parameters with K.
The first possible difference could just come from a scaling difference. If the second time you estimate your intrinsics matrix, you end up with a matrix
K_2=lambda*K
then it doesn't make any difference when projecting or reprojecting, since for any 3d point X you'll have
K_2*X=K*lambda*X //X is the same as lambda*X in projective geometry
The same thing happens when you backproject the point: you just obtain a direction, and then your estimation algorithm (e.g. least squares or a simpler geometric solution) takes care of estimating the depth.
The second reason for the difference you observe could just come from numerical imprecisions. Since you haven't given any information regarding the magnitude of the difference, I'm not sure if that is relevant to your case.
I have:
- a set of points of known size (in my case, only 6 points)
- a line characterized by x = s + t * r, where x, s and r are 3D vectors
I need to find the point closest to the given line. The actual distance does not matter to me.
I had a look at several different questions that seem related (including this one) and know how to solve this on paper from my highschool math classes. But I cannot find a solution without calculating every distance, and I am sure there has to be a better/faster way. Performance is absolutely crucial in my application.
One more thing: All numbers are integers (coordinates of points and elements of s and r vectors). Again, for performance reasons I would like to keep the floating-point math to a minimum.
You have to process every point at least once to know their distance. Unless you want to repeat the process many times with different lines, simply computing the distance of every point is unavoidable. So the algorithm has to be O(n).
Since you don't care about the actual distance, we can make some simplification to the point-distance computation. The exact distance is computed by (source):
d^2 = |r⨯(p-s)|^2 / |r|^2
where ⨯ is the cross product and |r|^2 is the squared length of vector r. Since |r|^2 is constant for all points, we can omit it from the distance computation without changing result:
d^2 = |r⨯(p-s)|^2
Compare the approximated square distances and keep the minimum. The advantage of this formula is that you can do everything with integers since you mentioned that all coordinates are integers.
I'm afraid you can't get away with computing less than 6 distances (if you could, at least one point would be left out -- including the nearest one).
See if it makes sense to preprocess: Is the line fixed and the points vary? Consider rotating coordinates to make the line horizontal.
As there are few points, it is doubtful that this is your bottleneck. Measure where the hot spots are, redesign algorithms/data representation, spice up compiler optimization, compile to assembly and bum that. Strictly in that order.
Jon Bentley's "Writing Efficient Programs" (sadly long out of print) and "Programming Pearls" (2nd edition) are full of advise on practical programming.
So, I have two points, say A and B, each one has a known (x, y) coordinate and a speed vector in the same coordinate system. I want to write a function to generate a set of arcs (radius and angle) that lead A to status B.
The angle difference is known, since I can get it by subtracting speed unit vector. Say I move a certain distance with (radius=r, angle=theta) then I got into the exact same situation. Does it have a unique solution? I only need one solution, or even an approximation.
Of course I can solve it by giving a certain circle and a line(radius=infine), but that's not what I want to do. I think there's a library that has a function for this, since it's quite a common approach.
A biarc is a smooth curve consisting of two circular arcs. Given two points with tangents, it is almost always possible to construct a biarc passing through them (with correct tangents).
This is a very basic routine in geometric modelling, and it is indispensable for smoothly approximating an arbirtrary curve (bezier, NURBS, etc) with arcs. Approximation with arcs and lines is heavily used in CAM, because modellers use NURBS without a problem, but machine controllers usually understand only lines and arcs. So I strongly suggest reading on this topic.
In particular, here is a great article on biarcs on biarcs, I seriously advice reading it. It even contains some working code, and an interactive demo.
I'm looking for interpolating some contour lines to generating a 3D view. The contours are not stored in a picture, coordinates of each point of the contour are simply stored in a std::vector.
for convex contours :
, it seems (I didn't check by myself) that the height can be easily calculates (linear interpolation) by using the distance between the two closest points of the two closest contours.
my contours are not necessarily convex :
, so it's more tricky... actualy I don't have any idea what kind of algorithm I can use.
UPDATE : 26 Nov. 2013
I finished to write a Discrete Laplace example :
you can get the code here
What you have is basically the classical Dirichlet problem:
Given the values of a function on the boundary of a region of space, assign values to the function in the interior of the region so that it satisfies a specific equation (such as Laplace's equation, which essentially requires the function to have no arbitrary "bumps") everywhere in the interior.
There are many ways to calculate approximate solutions to the Dirichlet problem. A simple approach, which should be well suited to your problem, is to start by discretizing the system; that is, you take a finite grid of height values, assign fixed values to those points that lie on a contour line, and then solve a discretized version of Laplace's equation for the remaining points.
Now, what Laplace's equation actually specifies, in plain terms, is that every point should have a value equal to the average of its neighbors. In the mathematical formulation of the equation, we require this to hold true in the limit as the radius of the neighborhood tends towards zero, but since we're actually working on a finite lattice, we just need to pick a suitable fixed neighborhood. A few reasonable choices of neighborhoods include:
the four orthogonally adjacent points surrounding the center point (a.k.a. the von Neumann neighborhood),
the eight orthogonally and diagonally adjacent grid points (a.k.a. the Moore neigborhood), or
the eight orthogonally and diagonally adjacent grid points, weighted so that the orthogonally adjacent points are counted twice (essentially the sum or average of the above two choices).
(Out of the choices above, the last one generally produces the nicest results, since it most closely approximates a Gaussian kernel, but the first two are often almost as good, and may be faster to calculate.)
Once you've picked a neighborhood and defined the fixed boundary points, it's time to compute the solution. For this, you basically have two choices:
Define a system of linear equations, one per each (unconstrained) grid point, stating that the value at each point is the average of its neighbors, and solve it. This is generally the most efficient approach, if you have access to a good sparse linear system solver, but writing one from scratch may be challenging.
Use an iterative method, where you first assign an arbitrary initial guess to each unconstrained grid point (e.g. using linear interpolation, as you suggest) and then loop over the grid, replacing the value at each point with the average of its neighbors. Then keep repeating this until the values stop changing (much).
You can generate the Constrained Delaunay Triangulation of the vertices and line segments describing the contours, then use the height defined at each vertex as a Z coordinate.
The resulting triangulation can then be rendered like any other triangle soup.
Despite the name, you can use TetGen to generate the triangulations, though it takes a bit of work to set up.
I am trying to fit a curve to a number of pixels in an image so I can do further processing regarding it's shape. Does anyone know how to implement a least squares method in C/++ preferably using the following parameters: an x array, a y array, and an answers array (the length of the answers array should tell how many coefficients need to be calculated)?
If this is not some exercise in implementing this yourself, I would suggest you use a ready-made library like GNU gsl. Have a look at the functions whose names start with gsl_multifit_, see e.g. the second example here.
If you are trying to fit ordered points (x,y) like in a graph you can use linear least squares methods but always with such methods you will need to specify the degree of the polynomial you use to approximate with (length of your answers array presumably). If your points are general ordered points in the plane that are able to form a closed loop or some outline of a structure (for example trying to fit points that describe an ellipse or a circle or other closed or more complex geometry) then you are going to need something more sophisticated. You can still use least squares but you will need to use a parametric type curve like a spline. Take a look at the pdf at this link which may give what you need (or at the very least illustrate what I am saying): http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0CE0QFjAA&url=http%3A%2F%2Ffolk.uio.no%2Fin329%2Fnchap6.pdf&ei=Yp8CUNvHC8Kg0QX6r_mEBw&usg=AFQjCNHBUZ5t2Y7C8eONYSosRydLs4Zu4A
Without seeing an image of exactly what you are trying to fit it is hard to say - it is quite possible that your data can be fit in a non parametric way with linear least squares polynomials - if so all you will need is a linear algebra library and you can code the approximations yourself like so: http://en.wikipedia.org/wiki/Ordinary_least_squares
Even so, all forms of approximation require you to decide on your form (function basis and degree etc) before you fit it. For example, if you want to decide on whether you need a 4th,5th,6th or 7th degree polynomial fit your data you would need to fit each one and assess the suitability for yourself. There is no generic way (at least none that I know of) that will tell you the degree of approximation you need to fit to your data.