I'm trying to think of the best way to conduct some sort of analysis between two 3D models of the same object.
The first scan is of the original item and the second scan is after it has been put under some load x.
An example would be trying to find the difference between two types of metal.
I would like to be able to scan the initial metal cylinder, apply a measured load, scan it again, and then finally apply some sort of algorithm to compare the difference.
Is it possible to do this efficiently (maybe using Mablab) over say 50 - 100 items for an object around 5inch^3?
I am assuming I will need to work out some sort of utility function as the total mass should be the same?
Would machine learning be beneficial in this case?
Any suggestions or direction would be amazing.
Thank you :)
EDIT: The scan files are coming through as '.stl'
currently, i have a table with locations (latitude, longitude). I calculate nearby calculations using sin, cos as described here
This seems rather slow. I am having the idea of pre-calculating the distance to a fixed point f and store it along the locations. When I now want to find locations nearby i just calculate distance to the same fix point and can then find them by doing some less or equal comparing.
Does my idea make sense? Is there a standard way to do that? I am in the thinking phase, so i do not have any code to show yet.
Your idea won't work unless all your locations are collinear, which most probably is not the case.
Are you using SQL to do the calculations? Are you properly using indexes? Maybe you could share a bit of your code with us.
I'm working with openCV and I'm a newbie in this field. I'm researching about Camshift. I want to extend this method by using multiple histograms. It means when tracking an object has many than one apperance (ex: rubik cube with six apperance), if we use only one histogram, Camshift will most likely fail.
I know calcHist function in openCV (http://docs.opencv.org/modules/imgproc/doc/histograms.html#calchist) has a parameter is "accumulate", but I don't know how to use and when to use (apply for camshiftdemo.cpp in opencv samples folder). This function can help me solve this problem? Or I have to use difference solution?
I have an idea, that is: create an array histogram for object, for every appearance condition that strongly varies in color, we pre-compute and store all to this array. But when we compute new histogram? It means that the pre-condition to start compute new histogram is what?
And what happend if I have to track multiple object has same color?
Everybody please help me. Thank you so much!
my Application is given a list of Geocorrdinates and now I have to determine which of those Coordinates are inside a defined Area. For example the Search would definiton would be: Show me all Areas where 100 Coordinates are in an Area of 1km^2. So I have to find out which of these coordinates are together in Areas of 1km^2 and more than 100.
But that seems to be a hard Job for my understanding of geocoordinates and I hope someone can help me with that.
The Latitude coordinates are consistent and Distance between two degrees is 111km. For example the Distance between N50,985° and N50,995° is 1,11km. The Distance between 2 Longtitude Coordinates is not so easy and depends on the Latitude coordinate.
But to be honest, I really don't know how to start.
Does someone have an Idea and can help me?
Thank you
twickl
what you need is a geospatial database, I'd recommend PostgreSQL with PostGIS. It provides the function you need to calculate this kind of stuff. Also search for good tutorials about it. An example is a radius search like "give me all McDonalds in a radius of 10km where I live"
If the problem with having a database is simply that you don't want to host it (or pay for someone to host it) then I recommend Fusion Tables.
I don't know if it supports exactly the functionality you are looking for, however I suppose you could select a random point and do a count of everything within range of that point.
I think that what you are talking could be quite a cpu intensive task (depending on how accurate you want it to be). Not something I would personally try and unload onto a portable device.
I am developing a game for the web. The map of this game will be a minimum of 2000km by 2000km. I want to be able to encode elevation and terrain type at some level of granularity - 100m X 100m for example.
For a 2000km by 2000km map storing this information in 100m2 buckets would mean 20000 by 20000 elements or a total of 400,000,000 records in a database.
Is there some other way of storing this type of information?
MORE INFORMATION
The map itself will not ever be displayed in its entirety. Units will be moved on the map in a turn based fashion and the players will get feedback on where they are located and what the local area looks like. Terrain will dictate speed and prohibition of movement.
I guess I am trying to say that the map will be used for the game and not necessarily for a graphical or display purposes.
It depends on how you want to generate your terrain.
For example, you could procedurally generate it all (using interpolation of a low resolution terrain/height map - stored as two "bitmaps" - with random interpolation seeded from the xy coords to ensure that terrain didn't morph), and use minimal storage.
If you wanted areas of terrain that were completely defined, you could store these separately and use them where appropriate, randomly generating the rest.)
If you want completely defined terrain, then you're going to need to look into some kind of compression/streaming technique to only pull terrain you are currently interested in.
I would treat it differently, by separating terrain type and elevation.
Terrain type, I assume, does not change as rapidly as elevation - there are probably sectors of the same type of terrain that stretch over much longer than the lowest level of granularity. I would map those sectors into database records or some kind of hash table, depending on performance, memory and other requirements.
Elevation I would assume is semi-contiuous, as it changes gradually for the most part. I would try to map the values into set of continuous functions (different sets between parts that are not continues, as in sudden change in elevation). For any set of coordinates for which the terrain is the same elevation or can be described by a simple function, you just need to define the range this function covers. This should reduce much the amount of information you need to record to describe the elevation at each point in the terrain.
So basically I would break down the map into different sectors which compose of (x,y) ranges, once for terrain type and once for terrain elevation, and build a hash table for each which can return the appropriate value as needed.
If you want the kind of granularity that you are looking for, then there is no obvious way of doing it.
You could try a 2-dimensional wavelet transform, but that's pretty complex. Something like a Fourier transform would do quite nicely. Plus, you probably wouldn't go about storing the terrain with a one-record-per-piece-of-land way; it makes more sense to have some sort of database field which can store an encoded matrix.
I think the usual solution is to break your domain up into "tiles" of manageable sizes. You'll have to add a little bit of logic to load the appropriate tiles at any given time, but not too bad.
You shouldn't need to access all that info at once--even if each 100m2 bucket occupied a single pixel on the screen, no screen I know of could show 20k x 20k pixels at once.
Also, I wouldn't use a database--look into height mapping--effectively using a black & white image whose pixel values represent heights.
Good luck!
That will be awfully lot of information no matter which way you look at it. 400,000,000 grid cells will take their toll.
I see two ways of going around this. Firstly, since it is a web-based game, you might be able to get a server with a decently sized HDD and store the 400M records in it just as you would normally. Or more likely create some sort of your own storage mechanism for efficiency. Then you would only have to devise a way to access the data efficiently, which could be done by taking into account the fact that you doubtfully will need to use it all at once. ;)
The other way would be some kind of compression. You have to be careful with this though. Most out-of-the-box compression algorithms won't allow you to decompress an arbitrary location in the stream. Perhaps your terrain data has some patterns in it you can use? I doubt it will be completely random. More likely I predict large areas with the same data. Perhaps those can be encoded as such?