I have a tof camera (pmd camboard nano), and my goal it's to between meshes calculate the distance from each other to calculate the deformation distance and the 3d position of specific points.
What is the best method to make that? I try with ruler, and euclidian distance in matlab with the point source, but i want the calcultion to be more precise.
Here's a solution, assuming both datasets have exactly the same number of points and you are comparing point coordinates for points at the same index:
Apply the Calculator filter on both the datasets separately with the expression coords. This will create new datasets with Result array in the PointData that corresponds to the point locations for each of the datasets.
Select the two calculator filters and then apply the Python Calculator filter with expression set to sqrt(sum((inputs[0].PointData["Result"] - inputs[1].PointData["Result"])**2)).
The resulting dataset will have a result array which is the euclidean distance between the two corresponding points.
To limit this calculation to specific points, you can use the Extract Selection or Threshold to extract smaller datasets with points of interest and then do the above.
Related
Lets some objects make complex spiral moving in 3D and we have get their trajectories projected on a plane.
How to find a median trajectory of such movements and estimate the amplitudes of spirals?
I assume that this requires averaging the coordinates of the trajectories, then somehow finding the distances from the extreme points of the trajectories to the midline. But I don't know a concrete algorithm for this. Can someone suggest this algorithm?
By median trajectory I mean a line that ges between path waves, something like linew on a picture below.
This is a case for a Kalman filter, but this method is a little complicated.
A simpler one is a moving average, with a number of samples that covers as closes as possible to a full period (which you can estimate visually).
Regarding the distances, you can compute the shortest Euclidean distance of every point to the midline (using a line-to-segment function). This will yield an alternating plot, which you can smoothen with a moving maximum (rather than average) over a period.
I have a set of x,y coordinates and intensity (x,y,I). These x,y coordinates are not uniform and do not form an ordered 2d array. I'd like to 2D median filter the intensity based on the x,y coordinates . Unfortunately I cant simply use medfilt2 because they are not ordered or uniformly spaced.
What I tried:
make a map of the neighborhood of each (x,y) coordinate:
for i=1:numel(x)
neighbo{i}=find(sqrt( (x-x(i)).^2+(y-y(i)).^2)<150);
end
this is already a problem as the the vector x is 1e7 long, and it takes really a long time just to do that.
next I'd sort each neighbo{i} and pick the central value, I can just imagine it would take just as long.
Any advice on how to achieve that median filtering, or make the above more efficient ?
In part of an Artificial Neural Network matlab code, I want to find nearest points of two convex polygons.
I saw
dsearchn(X,T,XI)
command's description here, but that finds closest points between two sets of points, and polygons (like convexs) have infinites points.
So can you suggest any way/idea?
Notice: I'm using MATLAB 2014a. I have the coordinate of each convex's vertex point.
If you are not happy with what is provided by dsearchn, then, If I were you, I would do one of two following:
Find Nearest Neighbours on the vertices (for example which vertex of
polygon A is the NN of a given vertex of polygon B).
Pick a random point inside polygon A (you may want to compute the
convex hull of A, but you may skip that and take into account only
the vertices you know already). That random point is the query. Find
an NN of that point from the vertices of polygon B.
You may want to ask in Software recommendations for more.
Edit:
Another approach is this:
Create a representative dataset of polygon A. Set the size of the dataset yourself and fill it with samples of points that lie inside the polygon. Choose them uniformly randomly inside the polygon.
Then take a point of polygon B (either a vertex or a random point inside polygon B) and that's the query point, for which you will seek Nearest Neighbour(s) inside the representative dataset of polygon A.
Of course that's just an approximation, but I can't think of something else now.
Notice that you can of course do the same for polygon B.
With This File in File Exchange, I've find the solution.
With a little code modification, I have drawn a perpendicular bisector which I wanted. Of course, this way is time consuming.
There are a lot of similar questions but I can't get a clear answer out of them. So, I want to represent latitude and longitude in a 2D space such that I can calculate the distances if necessary.
There is the equirectangular approach which can calculate the distances but this is not exactly what I want.
There is the UTM but it seems there are many zones and letters. So the distance should take into consideration the changing of zone which is not trivial.
I want to have a representation such that i can deal with x,y as numbers in Euclidean space and perform the standard distance formula on them without multiplying with the diameter of Earth every time I need to calculate the distance between two points.
Is there anything in Matlab that can change lat/long to x,y in Euclidean space?
I am not a matlab speciallist but the answer is not limited to matlab. Generally in GIS when you want to perform calculations in Euclidean space you have to apply 'projection' to the data. There are various types of projections, one of the most popular being Transverse Mercator
The common feature of such projections is the fact you can't precisely represent whole world with it. I mean the projection is based on chosen meridian and is precise enough up to some distance from it (e.g. Gauss Krueger projection is quite accurate around +-500km from the meridian.
You will always have to choose some kind of 'zone' or 'meridian', regardless of what projection you choose, because it is impossible to transform a sphere into plane without any deformations (be it distance, angle or area).
So if you are working on a set of data located around some geographical area you can simply transform (project) the data and treat it as normal Enclidean 2d space.
But if you think of processing data located around the whole world you will have to properly cluster and project it using proper zone.
I need to evaluate the proximity of a Point to a LineString using MongoDB.
Because the $near operator can only compare a Point to another Point, I need to generate a polygon out of the LineString, so I can use the $within operator. The distance between the LineString and the edges of the polygon should represent the radius I want to search in, such as represented in red below:
What might be a useful algorithm in order to accomplish this?
I think much easier would be to write your own function
To find (perpendicular) distance between point and line and then creating thickness of poly-line by polygon means.
Where:
P0,P1 are line endpoints
P is point
d is distance between them
Line is defined as: p(t)=P0+(P1-P0)*t where t=<0.0,1.0>
So the function should do this:
create perpendicular line
q(t)=P+DQ*u where u=(-inf,+inf)
DQ is perpendicular vector to (P1-P0)
In 2D you can obtain it easily like this (x,y) -> (y,-x). In higher dimensions use cross product with some non coplanar vectors.
compute line vs. line intersection
there are tons of stuff about this so google or solve the equation yourself here you can extract mine implementation.
now after successful intersection
just compute d as distance between P and intersection point. Do not forget that parameter t must be in range. If not (or if no intersection) then return min(|P-P0|,|P-P1|)
[hints]
t and u parameters can be obtained directly from intersection test so if the perpendicular vector to (P1-P0) is normalized to size = 1 then the abs(u) parameter of intersection point is the distance
[notes]
I am not familiar with mongodb so if you have no means to use own tests inside then this answer is of coarse obsolete.
Unfortunately, MongoDB provides very basic geospatial query, so you should create the buffer by your own. You can read how to do it here: Computing a polygon that surrounds a multi-point line
If you have longitude/latitude coordinates like WGS84 you must adjust this code; Read here how to calculate distance between point on a sphere https://en.wikipedia.org/wiki/Haversine_formula