I want to create a UI to do polygon editing. My input is a vector image (cubic bezier). The main goal is users are able to draw scribbles to create new segments on the polygon, so it's like semi-automatic polygon drawing by considering the existing polygon.
Based on assumption the users input is drawn using a mouse, so it's typically rough and quite imprecise.
The new segments aren't only straight lines but also curves, So I think the new segments should also conform the rough input of the users:
Another case:
Is this solvable? Any feedback is really appreciated, I also want to know if there's any paper with similar problem.
Here is way to start with :-
Do connected components on the image to get user scribbles
try and fit the scribbles to line,circle,parabola..
Whichever gives the minimum squared error is the desired curve
Draw the desired curve between the endpoints of scribble.
References:-
Connected components
Curve Fitting
Solved the problem,
I used Euler Spiral
http://www.lems.brown.edu/vision/researchAreas/EulerSpiral/
Related
I want to create a NurbsSurface in OpenGL. I use a grid of control points size of 40x48. Besides I create indices in order to determine the order of vertices.
In this way I created my surface of triangles.
Just to avoid misunderstandings. I have
float[] vertices=x1,y1,z1,x2,y2,z2,x3,y3,z3....... and
float[] indices= 1,6,2,7,3,8....
Now I don't want to draw triangles. I would like to interpolate the surface points. I thought about nurbs or B-Splines.
The clue is:
In order to determine the Nurbs algorithms I have to interpolate patch by patch. In my understanding one patch is defined as for example points 1,6,2,7 or 2,7,3,8(Please open the picture).
First of all I created the vertices and indices in order to use a vertexshader.
But actually it would be enough to draw it on the old way. In this case I would determine vertices and indices as follows:
float[] vertices= v1,v2,v3... with v=x,y,z
and
float[] indices= 1,6,2,7,3,8....
In OpenGL, there is a Nurbs function ready to use. glNewNurbsRenderer. So I can render a patch easily.
Unfortunately, I fail at the point, how to stitch the patches together. I found an explanation Teapot example but (maybe I have become obsessed by this) I can't transfer the solution to my case. Can you help?
You have set of control points from which you want to draw surface.
There are two ways you can go about this
Which is described in Teapot example link you have provided.
Calculate the vertices from control points and pass then down the graphics
pipeline with GL_TRIANGLE as topology. Please remember graphics hardware
needs triangulated data in order to draw.
Follow this link which shows how to evaluate vertices from control points
http://www.glprogramming.com/red/chapter12.html
You can prepare path of your control points and use tessellation shaders to
triangulate and stitch those points.
For this you prepare set of control points as patch use GL_PATCH primitive
and pass it to tessellation control shader. In this you will specify what
tessellation level you want. Depending on that your patch will be tessellated
by another fixed function stage known as Primitive Generator.
Then your generated vertices will be pass to tessellation evaluation shader
in which you can fine tune. Here you can specify outer or inner tessellation
level which will further subdivide your patch.
I would suggest you put your VBO and IBO like you have with control points and when drawing use GL_PATCH primitive. Follow below tutorial about how to use tessellation shader to draw nurb surfaces.
Note : Second method I have suggested is kind of tricky and you will have to read lot of research papers.
I think if you dont want to go with modern pipeline then I suggest go with option 1.
I have a .xyz file that has irregularly spaced points and gives the position and surface normal (ie XYZIJK). Are there algorithms out there that can reconstruct the surface that factor in the IJK vectors? Most algorithms I have found assume that surface normals aren't known.
This would ultimately be used to plot surface error data (from the nominal surface) using python 3.x, and I'm sure I will have many more follow on questions once I find a good reconstruction algorithm.
The state of the art right now is Poisson Surface Reconstruction and its screened variant. Code for both is available, e.g. under http://www.cs.jhu.edu/~misha/Code/PoissonRecon/Version8.0/. It is also implemented in MeshLab if you want to take a quick look.
If you want to take a look at other methods, check out this STAR. Page three has a table of a couple of approaches and their inputs.
I have set of binary images, on which i need to find the cross (examples attached). I use findcontours to extract borders from the binary image. But i can't understand how can i determine is this shape (border) cross or not? Maybe opencv has some built-in methods, which could help to solve this problem. I thought to solve this problem using Machine learning, but i think there is a simpler way to do this. Thanks!
Viola-Jones object detection could be a good start. Though the main usage of the algorithm (AFAIK) is face detection, it was actually designed for any object detection, such as your cross.
The algorithm is Machine-Learning based algorithm (so, you will need a set of classified "crosses" and a set of classified "not crosses"), and you will need to identify the significant "features" (patterns) that will help the algorithm recognize crosses.
The algorithm is implemented in OpenCV as cvHaarDetectObjects()
From the original image, lets say you've extracted sets of polygons that could potentially be your cross. Assuming that all of the cross is visible, to the extent that all edges can be distinguished as having a length, you could try the following.
Reject all polygons that did not have exactly 12 vertices required to
form your polygon.
Re-order the vertices such that the shortest edge length is first.
Create a best fit perspective transformation that maps your vertices onto a cross of uniform size
Examine the residuals generated by using this transformation to project your cross back onto the uniform cross, where the residual for any given point is the distance between the projected point and the corresponding uniform point.
If all the residuals are within your defined tolerance, you've found a cross.
Note that this works primarily due to the simplicity of the geometric shape you're searching for. Your contours will also need to have noise removed for this to work, e.g. each line within the cross needs to be converted to a single simple line.
Depending on your requirements, you could try some local feature detector like SIFT or SURF. Check OpenSURF which is an interesting implementation of the latter.
after some days of struggle, i came to a conclusion that the only robust way here is to use SVM + HOG. That's all.
You could erode each blob and analyze their number of pixels is going down. No mater the rotation scaling of the crosses they should always go down with the same ratio, excepted when you're closing down on the remaining center. Again, when the blob is small enough you should expect it to be in the center of the original blob. You won't need any machine learning algorithm or training data to resolve this.
To give you some background as to what I'm doing: I'm trying to quantitatively record variations in flow of a compressible fluid via image analysis. One way to do this is to exploit the fact that the index of refraction of the fluid is directly related to its density. If you set up some kind of image behind the flow, the distortion in the image due to refractive index changes throughout the fluid field leads you to a density gradient, which helps to characterize the flow pattern.
I have a set of routines that do this successfully with a regular 2D pattern of dots. The dot pattern is slightly distorted, and by comparing the position of the dots in the distorted image with that in the non-distorted image, I get a displacement field, which is exactly what I need. The problem with this method is resolution. The resolution is limited to the number of dots in the field, and I'm exploring methods that give me more data.
One idea I've had is to use a regular grid of horizontal and vertical lines. This image will distort the same way, but instead of getting only the displacement of a dot, I'll have the continuous distortion of a grid. It seems like there must be some standard algorithm or procedure to compare one geometric grid to another and infer some kind of displacement field. Nonetheless, I haven't found anything like this in my research.
Does anyone have some ideas that might point me in the right direction? FYI, I am not a computer scientist -- I'm an engineer. I say that only because there may be some obvious approach I'm neglecting due to coming from a different field. But I can program. I'm using MATLAB, but I can read Python, C/C++, etc.
Here are examples of the type of images I'm working with:
Regular: Distorted:
--------
I think you are looking for the Digital Image Correlation algorithm.
Here you can see a demo.
Here is a Matlab Implementation.
From Wikipedia:
Digital Image Correlation and Tracking (DIC/DDIT) is an optical method that employs tracking & image registration techniques for accurate 2D and 3D measurements of changes in images. This is often used to measure deformation (engineering), displacement, and strain, but it is widely applied in many areas of science and engineering.
Edit
Here I applied the DIC algorithm to your distorted image using Mathematica, showing the relative displacements.
Edit
You may also easily identify the maximum displacement zone:
Edit
After some work (quite a bit, frankly) you can come up to something like this, representing the "displacement field", showing clearly that you are dealing with a vortex:
(Darker and bigger arrows means more displacement (velocity))
Post me a comment if you are interested in the Mathematica code for this one. I think my code is not going to help anybody else, so I omit posting it.
I would also suggest a line tracking algorithm would work well.
Simply start at the first pixel line of the image and start following each of the vertical lines downwards (You just need to start this at the first line to get the starting points. This can be done by a simple pattern that moves orthogonally to the gradient of that line, ergo follows a line. When you reach a crossing of a horizontal line you can measure that point (in x,y coordinates) and compare it to the corresponding crossing point in your distorted image.
Since your grid is regular you know that the n'th measured crossing point on the m'th vertical black line are corresponding in both images. Then you simply compare both points by computing their distance. Do this for each line on your grid and you will get, by how far each crossing point of the grid is distorted.
This following a line algorithm is also used in basic Edge linking algorithms or the Canny Edge detector.
(All this are just theoretic ideas and I cannot provide you with an algorithm to it. But I guess it should work easily on distorted images like you have there... but maybe it is helpful for you)
I'm searching for an certain object in my photograph:
Object: Outline of a rectangle with an X in the middle. It looks like a rectangular checkbox. That's all. So, no fill, just lines. The rectangle will have the same ratios of length to width but it could be any size or any rotation in the photograph.
I've looked a whole bunch of image recognition approaches. But I'm trying to determine the best for this specific task. Most importantly, the object is made of lines and is not a filled shape. Also, there is no perspective distortion, so the rectangular object will always have right angles in the photograph.
Any ideas? I'm hoping for something that I can implement fairly easily.
Thanks all.
You could try using a corner detector (e.g. Harris) to find the corners of the box, the ends and the intersection of the X. That simplifies the problem to finding points in the right configuration.
Edit (response to comment):
I'm assuming you can find the corner points in your image, the 4 corners of the rectangle, the 4 line endings of the X and the center of the X, plus a few other corners in the image due to noise or objects in the background. That simplifies the problem to finding a set of 9 points in the right configuration, out of a given set of points.
My first try would be to look at each corner point A. Then I'd iterate over the points B close to A. Now if I assume that (e.g.) A is the upper left corner of the rectangle and B is the lower right corner, I can easily calculate, where I would expect the other corner points to be in the image. I'd use some nearest-neighbor search (or a library like FLANN) to see if there are corners where I'd expect them. If I can find a set of points that matches these expected positions, I know where the symbol would be, if it is present in the image.
You have to try if that is good enough for your application. If you have too many false positives (sets of corners of other objects that accidentially form a rectangle + X), you could check if there are lines (i.e. high contrast in the right direction) where you would expect them. And you could check if there is low contrast where there are no lines in the pattern. This should be relatively straightforward once you know the points in the image that correspond to the corners/line endings in the object you're looking for.
I'd suggest the Generalized Hough Transform. It seems you have a fairly simple, fixed shape. The generalized Hough transform should be able to detect that shape at any rotation or scale in the image. You many need to threshold the original image, or pre-process it in some way for this method to be useful though.
You can use local features to identify the object in image. Feature detection wiki
For example, you can calculate features on some referent image which contains only the object you're looking for and save the results, let's say, to a plain text file. After that you can search for the object just by comparing newly calculated features (on images with some complex scenes containing the object) with the referent ones.
Here's some good resource on local features:
Local Invariant Feature Detectors: A Survey