I need to obtain the intersection of two curves. The problem I'm facing can be stated in the following way:
Given two curves C1 and C2, defined by N1 and N2 points connected by straight lines, obtain all the intersections of C1 with C2. Both curves don't intersect themselves.
I tried several approaches, but none seems to work so far. Any guess?
The easiest way is to test all pairs of segments, one from each curve. If that is too slow, try a strip tree. The paper below can be found at the author's web site.
Ballard, D. H. (1981), Strip trees: a hierarchical representation for
curves, Communications of the ACM, v.24 n.5,310-321
Since your curves are comprised of line segments, I would suggest using a spatial tree (e.g. a quadtree) to only check segments that are in proximity to each other. This will reduce the complexity of your algorithm from O(N1 N2) to O(N log N) (where N = N1 + N2), assuming that the number of very close intersections is small.
Other than that, you can find intersections in this way.
Related
For 2-dimensional sampled curves (an array of 2D points) there exists the Rahmer-Douglas-Peucker algorithm which only keeps "important" points. It works by calculating the perpendicular distance of each point (or sample) to a line that connects the first and the last point of. If the maximum distance is larger than a value epsilon the point is kept and the array is split into 2 parts. For both parts the operation is repeated (maximal perpendicular distance, if larger than epsilon etc.) The smaller epsilon the more detail is kept.
I am trying to write a function that can also do this for higher arrays of higher dimensional points. But I am unsure how to define distance. Or if this is actually a good idea.
I guess there exist lots of complicated and elegant algorithms that fit the curves to beziers and NURBS and what not. But are there also relatively simple ones?
I would prefer not to use beziers, but simply to identify "important" N-dimensional points.
You could extend your 2D algorithm using algebra and the L2 norm. Let's say you want to calculate the distance from a point X to a line segment PQ (where X, P and Q are defined as N-dimensional vectors).
First you can calculate the vector "proj" as:
Then, the distance is the module of the vector V = PX-proj.
For this calculation you only need the dot product between vectors, and that is well defined for N-dimensional spaces.
Using this approach I have successfuly used Rahmer-Douglas-Peucker algorithm in 3D.
Given set of N points, what is the maximum number of directed graphs can be created ? I'm having trouble with isomorphic problem.
Edit (1): Only directed simple, non-loop vertex graph, doesn't required to be connected
Edit (2): Any point in this set is treated equally to each other, so the main problem here is to calculate and subtract the number of isomorphic graphs created from different sets of edges.
Number of unlabeled directed graphs with n vertices is here (OEIS A000273)
1, 1, 3, 16, 218, 9608, 1540944, 882033440, 1793359192848
There is no closed formula, approximated value is number of labeled graphs divided by number of vertex permutations:
2^(n*(n-1)) / n!
There are n-1 possible edges for each node, so a total of n(n-1) edges.
Each possible graph will either contain a particular edge, or it won't.
So the number of possible graphs is 2^(n(n-1)).
EDIT: This only applies under the assumption there are no loops and each edge is unique.
Looping is basically coming back to the same node again so I'm considering double-headed arrows are not allowed. Now, if there are n nodes available so graphs you make without loops can have n-1 edges. Now, let m be the number of homeomorphic graphs you can make out of n nodes. Let si is number of symmetries present in ith graph of those m homeomorphic graphs. These symmetries I'm talking about are the likes of we study in group theory for geometric figures. Now, we know all edge can have 2 states i.e. left head and right head.
So the total number of distinct directed graphs can be given as:
Note: If these symmetries were not present then it would have been simply m*2(n-1)
(Edit 1) Also, this valid for connected graph with n nodes. If you want to include graphs that don't need to be connected then you'll have to modify a few things in this equation or add few things like the number of smaller partitions of this n noded graph you can form and apply this formula in each of those combinations.
Permutation&Combination, Group Theory, Symmetries, Partitions, Overall it's messy so this was the only simple way I could put it.
Given two simple polygons P and Q where P is convex but Q not, how fast can one compute the difference $P - Q$ between P and Q if P has n and Q has m vertices?
One can assume that the polygons are given as list of vertices ordered in clockwise direction.
"How fast" depends on lots of parameters, so I think, we should start with how to do it,first.
Firstly, I assume polygons lie on the same plane. Start with computing the finite intersection of each line of P with each line of Q. If the intersection exists and intersection point lies on intersecting lines(I mean between start and end, not on them), divide line into two and continue finite line-line intersections iteratively. Then, categorize each line segment(now I can call them as segment because that they are all divided if necessary) by using a point in polygon computation with mid-points of segments..Inner,Outer or OnthePolygon...After categorization construct a new polygon from the lines of P that lies outside of Q and lines of Q that lies inside of the P. Here, your challenge will be dealing with tolerances and lines that are lies on eachother..At first glance, overall algorithm is like this..
This algorithm can be improved by eliminating lines and even polygons by computing their ranges(min and max for each axis). Except from the hardware, programming language or data handling parameters, the speed of this operation is dependent on the input polygons and their orientation.
Given the points of a line and a quadratic bezier curve, how do you calculate their nearest point?
There exist a scientific paper regarding this question from INRIA: Computing the minimum distance between two Bézier curves (PDF here)
I once wrote a tool to do a similar task. Bezier splines are typically parametric cubic polynomials. To compute the square of the distance between a cubic segment and a line, this is just the square of the distance between two polynomial functions, itself just another polynomial function! Note that I said the square of the distance, not the square root.
Essentially, for any point on a cubic segment, one could compute the square of the distance from that point to the line. This will be a 6th order polynomial. Can we minimize that square of the distance? Yes. The minimum must occur where the derivative of that polynomial is zero. So differentiate, getting a 5th order polynomial. Use your favorite root finding tool that generates all of the roots numerically. Jenkins & Traub, whatever. Choose the correct solution from that set of roots, excluding any solutions that are complex, and only picking a solution if it lies inside the cubic segment in question. Make sure you exclude the points that correspond to local maxima of the distance.
All of this can be efficiently done, and no iterative optimizer besides a polynomial root finder need be used, thus one does not require the use of optimization tools that require starting values, finding only a solution near that starting value.
For example, in the 3-d figure I show a curve generated by a set of points in 3-d (in red), then I took another set of points that lay in a circle outside, I computed the closest point on the inner curve from each, drawing a line down to that curve. These points of minimum distance were generated by the scheme outlined above.
I just wanna give you a few hints, in for the case Q.B.Curve // segment :
to get a fast enough computation, i think you should first think about using a kind of 'bounding box' for your algorithm.
Say P0 is first point of the Q. B. Curve, P2 the second point, P1 the control point, and P3P4 the segment then :
Compute distance from P0, P1, P2 to P3P4
if P0 OR P2 is nearest point --> this is the nearest point of the curve from P3P4. end :=).
if P1 is nearest point, and Pi (i=0 or 1) the second nearest point, the distance beetween PiPC and P3P4 is an estimate of the distance you seek that might be precise enough, depending on your needs.
if you need to be more acurate : compute P1', which is the point on the Q.B.curve the nearest from P1 : you find it applying the BQC formula with t=0.5. --> distance from PiP1' to P3P4 is an even more accurate estimate -but more costly-.
Note that if the line defined by P1P1' intersects P3P4, P1' is the closest point of QBC from P3P4.
if P1P1' does not intersect P3P4, then you're out of luck, you must go the hard way...
Now if (and when) you need precision :
think about using a divide and conquer algorithm on the parameter of the curve :
which is nearest from P3P4 ?? P0P1' or P1'P2 ??? if it is P0P1' --> t is beetween 0 and 0.5 so compute Pm for t=0.25.
Now which is nearest from P3P4?? P0Pm or PmP1' ?? if it is PmP1' --> compute Pm2 for t=0.25+0.125=0.375 then which is nearest ? PmPm2 or Pm2P1' ??? etc
you will come to accurate solution in no time, like 6 iteration and your precision on t is 0.004 !! you might stop the search when distance beetween two points becomes below a given value. (and not difference beetwen two parameters, since for a little change in parameter, points might be far away)
in fact the principle of this algorithm is to approximate the curve with segments more and more precisely each time.
For the curve / curve case i would first 'box' them also to avoid useless computation, so first use segment/segment computation, then (maybe) segment/curve computation, and only if needed curve/curve computation.
For curve/curve, divide and conquer works also, more difficult to explain but you might figure it out. :=)
hope you can find your good balance for speed/accuracy with this :=)
Edit : Think i found for the general case a nice solution :-)
You should iterate on the (inner) bounding triangles of each B.Q.C.
So we have Triangle T1, points A, B, C having 't' parameter tA, tB, tC.
and Triangle T2, points D, E, F, having t parameter tD, tE, tF.
Initially we have tA=0 tB=0.5 tC= 1.0 and same for T2 tD=0, tE=0.5, tF=1.0
The idea is to call a procedure recursivly that will split T1 and/or T2 into smaller rectangles until we are ok with the precision reached.
The first step is to compute distance from T1 from T2, keeping track of with segments were the nearest on each triangle. First 'trick': if on T1 the segment is AC, then stop recursivity on T1, the nearest point on Curve 1 is either A or C. if on T2 the nearest segment is DF, then stop recursivity on T2, the nearest point on Curve2 is either D or F. If we stopped recursivity for both -> return distance = min (AD, AF, CD, CF). then if we have recursivity on T1, and segment AB is nearest, new T1 becomes : A'=A B= point of Curve one with tB=(tA+tC)/2 = 0.25, C=old B. same goes for T2 : apply recursivityif needed and call same algorithm on new T1 and new T2. Stop algorithm when distance found beetween T1 and T2 minus distance found beetween previous T1 and T2 is below a threshold.
the function might look like ComputeDistance(curveParam1, A, C, shouldSplitCurve1, curveParam2, D, F, shouldSplitCurve2, previousDistance) where points store also their t parameters.
note that distance (curve, segment) is just a particular case of this algorithm, and that you should implement distance (triangle, triangle) and distance (segment, triangle) to have it worked. Have fun.
1.Simple bad method - by iteration go by point from first curve and go by point from second curve and get minimum
2.Determine math function of distance between curves and calc limit of this function like:
|Fcur1(t)-Fcur2(t)| ->0
Fs is vector.
I think we can calculate the derivative of this for determine extremums and get nearest and farest points
I think about this some time later, and post full response.
Formulate your problem in terms of standard analysis: You have got a quantity to minimize (distance), so you formulate an equation for this quantity and find the points where the first derivatives are zero. Parameterize with a single parameter by using the curve's parameter p, which is between 0 for the first point and 1 for the last point.
In the line case, the equation is fairly simple: Get the x/y coordinates from the spline's equation and compute the distance to the given line via vector equations (scalar product with the line's normal).
In the curve's case, the analytical solution could get pretty complicated. You might want to use a numerical minimization technique such as Nelder-Mead or, since you have a 1D continuous problem, simple bisection.
In the case of a Bézier curve and a line
There are three candidates for the closest point to the line:
The place on the Bézier curve segment that is parallel to the line (if such a place exists),
One end of the curve segment,
The other end of the curve segment.
Test all three; the shortest distance wins.
In the case of two Bézier curves
Depends if you want the exact analytical result, or if an optimised numerical result is good enough.
Analytical result
Given two Bézier curves A(t) and B(s), you can derive equations for their local orientation A'(t) and B'(s). The point pairs for which A'(t) = B'(s) are candidates, i.e. the (t, s) for which the curves are locally parallel. I haven't checked, but I assume that A'(t) - B'(s) = 0 can be solved analytically. If your curves are anything like those you show in your example, there should be either only one solution or no solution to that equation, but there could be two (or infinitely many in the case where the curves identical but translated -- in which case you can ignore this because the winner will always be one of the curve segment endpoints).
In an approach similar to the curve-line case outline above, test each of these point pairs, plus the curve segment endpoints. The shortest distance wins.
Numerical result
Let's say the points on the two Bézier curves are defined as A(t) and B(s). You want to minimize the distance d( t, s) = |A(t) - B(s)|. It's a simple two-parameter optimization problem: find the s and t that minimize d( t, s) with the constraints 0 ≤ t ≤ 1 and 0 ≤ s ≤ 1.
Since d = SQRT( ( xA - xB)² + (yA - yB)²), you can also just minimize the function f( t, s) = [d( t, s)]² to save a square root calculation.
There are numerous ready-made methods for such optimization problems. Pick and choose.
Note that in both cases above, anything higher-order than quadratic Bézier curves can giver you more than one local minimum, so this is something to watch out for. From the examples you give, it looks like your curves have no inflexion points, so this concern may not apply in your case.
The point where there normals match is their nearest point. I mean u draw a line orthogonal to the line. .if that line is orthogonal to the curve as well then the point of intersection is the nearest point
I'm looking for an algorithm to find the common intersection points between 3 spheres.
Baring a complete algorithm, a thorough/detailed description of the math would be greatly helpful.
This is the only helpful resource I have found so far:
http://mathforum.org/library/drmath/view/63138.html
But neither method described there is detailed enough for me to write an algorithm on.
I would prefer the purely algebraic method described in the second post, but what ever works.
Here is an answer in Python I just ported from the Wikipedia article. There is no need for an algorithm; there is a closed form solution.
import numpy
from numpy import sqrt, dot, cross
from numpy.linalg import norm
# Find the intersection of three spheres
# P1,P2,P3 are the centers, r1,r2,r3 are the radii
# Implementaton based on Wikipedia Trilateration article.
def trilaterate(P1,P2,P3,r1,r2,r3):
temp1 = P2-P1
e_x = temp1/norm(temp1)
temp2 = P3-P1
i = dot(e_x,temp2)
temp3 = temp2 - i*e_x
e_y = temp3/norm(temp3)
e_z = cross(e_x,e_y)
d = norm(P2-P1)
j = dot(e_y,temp2)
x = (r1*r1 - r2*r2 + d*d) / (2*d)
y = (r1*r1 - r3*r3 -2*i*x + i*i + j*j) / (2*j)
temp4 = r1*r1 - x*x - y*y
if temp4<0:
raise Exception("The three spheres do not intersect!");
z = sqrt(temp4)
p_12_a = P1 + x*e_x + y*e_y + z*e_z
p_12_b = P1 + x*e_x + y*e_y - z*e_z
return p_12_a,p_12_b
Probably easier than constructing 3D circles, because working mainly on lines and planes:
For each pair of spheres, get the equation of the plane containing their intersection circle, by subtracting the spheres equations (each of the form X^2+Y^2+Z^2+aX+bY+c*Z+d=0). Then you will have three planes P12 P23 P31.
These planes have a common line L, perpendicular to the plane Q by the three centers of the spheres. The two points you are looking for are on this line. The middle of the points is the intersection H between L and Q.
To implement this:
compute the equations of P12 P23 P32 (difference of sphere equations)
compute the equation of Q (solve a linear system, or compute a cross product)
compute the coordinates of point H intersection of these four planes. (solve a linear system)
get the normal vector U to Q from its equation (normalize a vector)
compute the distance t between H and a solution X: t^2=R1^2-HC1^2, (C1,R1) are center and radius of the first sphere.
solutions are H+tU and H-tU
A Cabri 3D construction showing the various planes and line L
UPDATE
An implementation of this answer in python complete with an example of usage can be found at this github repo.
It turns out the analytic solution is actually quite nice using this method and can tell you when a solution exists and when it doesn't (it is also possible to have exactly one solution.) There is no reason to use Newton's method.
IMHO, this is far easier to understand and simpler than trilateration given below. However, both techniques give correct answers in my testing.
ORIGINAL ANSWER
Consider the intersection of two spheres. To visualize it, consider the 3D line segment N connecting the two centers of the spheres. Consider this cross section
(source: googlepages.com)
where the red-line is the cross section of the plane with normal N. By symmetry, you can rotate this cross-section from any angle, and the red line segments length can not change. This means that the resulting curve of the intersection of two spheres is a circle, and must lie in a plane with normal N.
That being said, lets get onto finding the intersection. First, we want to describe the resulting circle of the intersection of two spheres. You can not do this with 1 equation, a circle in 3D is essentially a curve in 3D and you cannot describe curves in 3D by 1 eq.
Consider the picture
(source: googlepages.com)
let P be the point of intersection of the blue and red line. Let h be the length of the line segment along the red line from point P upwards. Let the distance between the two centers be denoted by d. Let x be the distance from the small circle center to P. Then we must have
x^2 +h^2 = r1^2
(d-x)^2 +h^2 = r2^2
==> h = sqrt(r1^2 - 1/d^2*(r1^2-r2^2+d^2)^2)
i.e. you can solve for h, which is the radius of the circle of intersection. You can find the center point C of the circle from x, along the line N that joins the 2 circle centers.
Then you can fully describe the circle as (X,C,U,V are all vector)
X = C + (h * cos t) U + (h * sin t) V for t in [0,2*PI)
where U and V are perpendicular vectors that lie in a plane with normal N.
The last part is the easiest. It remains only to find the intersection of this circle with the final sphere. This is simply a plug and chug of the equations (plug in for x,y,z in the last equation the parametric forms of x,y,z for the circle in terms of t and solve for t.)
edit ---
The equation that you will get is actually quite ugly, you will have a whole bunch of sine's and cosine's equal to something. To solve this you can do it 2 ways:
write the cosine's and sine's in terms of exponentials using the equality
e^(it) = cos t + i sin t
then group all the e^(it) terms and you should get a quadratic equations of e^(it)'s
that you can solve for using the quadratic formula, then solve for t. This will give you the exact solution. This method will actually tell you exactly if a solution exists, two exist or one exist depending on how many of the points from the quadratic method are real.
use newton's method to solve for t, this method is not exact but its computationally much easier to understand, and it will work very well for this case.
Basically you need to do this in 3 steps. Let's say you've got three spheres, S1, S2, and S3.
C12 is the circle created by the intersection of S1 and S2.
C23 is the circle created by the intersection of S2 and S3.
P1, P2, are the intersection points of C12 and C13.
The only really hard part in here is the sphere intersection, and thankfully Mathworld has that solved pretty well. In fact, Mathworld also has the solution to the circle intersections.
From this information you should be able to create an algorithm.
after searching the web this is one of the first hits, so i am posting the most clean and easy solution i found after some hours of research here: Trilateration
This wiki site contains a full description of a fast and easy to understand vector approach, so one can code it with little effort.
Here is another interpretation of the picture which Eric posted above:
Let H be the plane spanned by the centers of the three spheres. Let C1,C2,C3 be the intersections of the spheres with H, then C1,C2,C3 are circles. Let Lij be the line connecting the two intersection points of Ci and Cj, then the three lines L12,L23,L13 intersect at one point P. Let M be the line orthogonal to H through P, then your two points of intersection lie on the line M; hence you just need to intersect M with either of the spheres.