I would like to do some odd geometric/odd shape recognition. But I'm not sure how to do it.
Here's what I have so far:
Convert RGB image to Monochrome.
Otsu Threshold
Hough Transform.
I'm not sure what to do next.
For geometric information, you could do a raster to vector conversion to convert your image into coordinated vectors (lines and points) and finite element analysis to look for known shapes. Not easy but libraries should be available for both.
Edit: Note that there are sometimes easier practical solutions, but they depend on the image and types of errors. For example, removing perspective, identifying a 3d object from a 2d image, significance of colour, etc... You often see registration markers added to the real world object to overcome
this and allow much easier identification. Looking up articles on feature extraction techniques might help.
Related
STL is the most popular 3d model file format for 3d printing. It records triangular surfaces that makes up a 3d shape.
I read the specification the STL file format. It is a rather simple format. Each triangle is represented by 12 float point number. The first 3 define the normal vector, and the next 9 define three vertices. But here's one question. Three vertices are sufficient to define a triangle. The normal vector can be computed by taking the cross product of two vectors (each pointing from a vertex to another).
I know that a normal vector can be useful in rendering, and by including a normal vector, the program doesn't have to compute the normal vectors every time it loads the same model. But I wonder what would happen if the creation software include wrong normal vectors on purpose? Would it produce wrong results in the rendering software?
On the other hand, 3 vertices says everything about a triangle. Include normal vectors will allow logical conflicts in the information and increase the size of file by 33%. Normal vectors can be computed by the rendering software under reasonable amount of time if necessary. So why should the format include it? The format was created in 1987 for stereolithographic 3D printing. Was computing normal vectors to costly to computers back then?
I read in a thread that Autodesk Meshmixer would disregard the normal vector and graph triangles according to the vertices. Providing wrong normal vector doesn't seem to change the result.
Why do Stereolithography (.STL) files require each triangle to have a normal vector?
At least when using Cura to slice a model, the direction of the surface normal can make a difference. I have regularly run into STL files that look just find when rendered as solid objects in any viewer, but because some faces have the wrong direction of the surface normal, the slicer "thinks" that a region (typically concave) which should be empty is part of the interior, and the slicer creates a "top layer" covering up the details of the concave region. (And this was with an STL exported from a Meshmixer file that was imported from some SketchUp source).
FWIW, Meshmixer has a FlipSurfaceNormals tool to help deal with this.
Softwares like Catia, SolidWorks or the like all can visualize complex models while designing.
Exporting such models to raster triangle meshes yields huge files that later need to be greatly simplified to be imported into 3D engines like Unreal Engine or equivalent.
My question is: how do they visualize such complex geometries without rasterization? How do they do it that fast?
GPUs can only deal with triangles, therefore they tessellate geometry exactly as for STL export. Tessellation tolerance may vary from display to STL export affecting the time required to compute it.
Exporting such models to raster triangle meshes yields huge files
Not entirely correct. When you ask solidworks for the mesh you also provide quality that will influence number of triangles you receive - can be millions, can be just a dozen.
CAD packages operate with most bodies/shapes analytically - they have a formula. My guess is any other 3D engine does the same, the thing is format of the analytical data that different engines use is not the same. So you need to convert from one to another using triangles, format that everybody understands.
i retrieve contours from images by using canny algorithm. it's enough to have a descriptor image and put in SVM and find similarities? Or i need necessarily other features like elongation, perimeter, area ?
I talk about this, because inspired by this example: http://scikit-learn.org/dev/auto_examples/plot_digits_classification.html i give my image in greyscale first, in canny algorithm style second and in both cases my confusion matrix was plenty of 0 like precision, recall, f1-score, support measure
My advice is:
unless you have a low number of images in your database and/or the recognition is going to be really specific (not a random thing for example) I would highly recommend you to apply one or more features extractors such SIFT, Fourier Descriptors, Haralick's Features, Hough Transform to extract more details which could be summarised in a short vector.
Then you could apply SVM after all this in order to get more accuracy.
I'm not really sure if this fits in here or better in a scientific computer science or math forum but since I'm searching for a concrete algorithm...
I have a 3d model which is somehow defined either by a mesh or as an algebraic variety and i want to remesh/approximate this thing just using a fixed chosen type of congruent tiles, e.g. isoscele triangles with certain ratio of sides length to the base length. Is there a algorithm for that or does anyone know the right name for the problem? I found some algorithms that come close to what I need, but they all mesh via some tolerance in the length and different sizes of the tiles.
In freeform shapes tiling is achieved via a very complicated algorithm. In real world architecture there is this method of tiling with as many identical tiles as possible and still get the shape, but there are angle tolerances and all sort of tolerances that you can manipulate. check paneling of freeform shapes.
Are there any classes, methods in the .NET library, or any algorithms in general, to perform non-affine transformations? (i.e. transformations that involve more than just rotation, scale, translation and shear)
e.g.:
(source: last100.com)
Is there another term for non-affine transformations?
I am not aware of anything integrated in .Net letting you do non affine transforms.
I guess you are trying to have some sort of 3D texture mapping? If that's the case you need an homogenous affine transform, which is not available in .Net. I'm also not aware of any integrated way to make pixel displacement transforms in .Net.
However, the currently voted solution might be good for what you are trying to do, just be aware that it won't do perspective correction out of the box.
For instance:
The picture on the left was generated using the single quad distort library provided by Neil N. The picture on the right was generated using a single quad (two triangles actually) in DirectX.
This may not have any impact on what you are trying to do, but this is something to keep in mind if you want to do 3D stuff, it will look very weird without perspective correct mapping.
All of the example images you posted can be done with a Quadrilateral Distortion. Though I cant say for certain that a quad distort will cover ALL non affine transforms.
Heres a link to a not so good implementation of it in C#... it works, but is slow. Poke around Wikipedia for the many different optimizations available for these kinds of calculations
http://www.vcskicks.com/image-distortion.html
-Neil
You can do this in wpf using a the Viewport3d control and a non-affine transform matrix. Rendering this to a bitmap again may be interesting.... Which I "fixed" by including an invisible <image> control with the same image as on my textured plane... (Also, I've had to work around the max texture size issues by splitting up the plane and cropping images...)
http://www.charlespetzold.com/blog/2007/08/060605.html
In my case I wanted the reverse of this (transform so arbitrary points on the warped become the corners of my rectangular window), which is the Inverse of the matrix to do the opposite.