Hi i want to transform a image like this (right to left image ):
I have searching about functions like cvCartToPolar but i dont know how to use it..
Can someone help me? :)
nowadays, there is cv::warpPolar and if you can't achieve what you want (because for example your input image is only part of a disk, you might be interessed in cv::remap (the former uses the later internally).
In the later case, you have to build the mapping table yourself with some math.
I am looking for some advice for a good way to detect either square or circular objects in an image. I currently have a canny edge algorithm running on the original greyscale and I can produce this output:
http://imgur.com/FAwowr1
Now I can see that there is a cubesat in this picture, but what is a good computationally efficient way that the program can see that aswell? I have looked at houghs transform but that seems to be very computation heavy. I have also looked at Harris corner detect, but I feel I would get to many false positives, for I am essentially looking to isolate pictures that contain said cube satellite.
Anyone have any thoughts on some good algorithms to pursue? I am very limited on space so I cannot use any large external libraries like opencv. (This is all in C btw)
Many Thanks!
I would into what is called mathematical morphology
Basically you operate on binary images, so you must find a clever way to threshold them first , the you do operations such as erosion and dilation with some well selected structuring element to extract areas of interest in your image.
i have a noisy set of data and want to find the peaks in it. There is a matlab function for this exact task which includes smoothing of the data. I is called findpeaks.m
Now as im working in C i would either would have to code this by myself or use some functions which im not aware of. I hope you can tell me if they exist and where i can find them, as this is a very common problem.
To be clear what im searching of: a function to first smooth my data and then calculate the peaks, both preferably with some parameters for smoothing method, peak width etc.
Thanks!
I'm interested in different algorithms people use to visualise millions of particles in a box. I know you can use Cloud-In-Cell, adaptive mesh, Kernel smoothing, nearest grid point methods etc to reduce the load in memory but there is very little documentation on how to do these things online.
i.e. I have array with:
x,y,z
1,2,3
4,5,6
6,7,8
xi,yi,zi
for i = 100 million for example. I don't want a package like Mayavi/Paraview to do it, I want to code this myself then load the decomposed matrix into Mayavi (rather than on-the-fly rendering) My poor 8Gb Macbook explodes if I try and use the particle positions. Any tutorials would be appreciated.
Analysing and creating visualisations for complex multi-dimensional data is complex. The best visualisation almost always depends on what the data is, and what relationships exists within the data. Of course, you are probably wanting to create visualisation of the data to show and explore relationships. Ultimately, this comes down to trying different posibilities.
My advice is to think about the data, and try to find sensible ways to slice up the dimensions. 3D plots, like surface plots or voxel renderings may be what you want. Personally, I prefer trying to find 2D representations, because they are easier to understand and to communicate to other people. Contour plots are great because they show 3D information in a 2D form. You can show a sequence of contour plots side by side, or in a timelapse to add a fourth dimension. There are also creative ways to use colour to add dimensions, while keeping the visualisation comprehensible -- which is the most important thing.
I see you want to write the code yourself. I understand that. Doing so will take a non-trivial effort, and afterwards, you might not have an effective visualisation. My advice is this: use a tool to help you prototype visualisations first! I've used gnuplot with some success, although I'm sure there are other options.
Once you have a good handle on the data, and how to communicate what it means, then you will be well positioned to code a good visualisation.
UPDATE
I'll offer a suggestion for the data you have described. It sounds as though you want/need a point density map. These are popular in geographical information systems, but have other uses. I haven't used one before, but the basic idea is to use a function to enstimate the density in a 3D space. The density becomes the fourth dimension. Something relatively simple, like the equation below, may be good enough.
The point density map might be easier to slice, summarise and render than the raw particle data.
The data I have analysed has been of a different nature, so I have not used this particular method before. Hopefully it proves helpful.
PS. I've just seen your comment below, and I'm not sure that this information will help you with that. However, I am posting my update anyway, just in case it is useful information.
first of all i should say i'm not very good in english so i hope you can get my point!
does any body works with A Vedaldi implementation of sift? my final project in university is about object recognition and i have to work with sift... i use A Vedaldi implementation and i can find features and descriptors for similar images...but i don't know how to find best features from for example 10 images and build a data base for a special object to be recognized later... plz help me :(
I have worked with Vedaldi's SIFT. I used to choose SIFT features at the edges or better at the corners of the image.
I find edges with canny, and corners with Harris detector. Then I keep points with a high score in corner/edges.
When you use Vedaldi's SIFT you can pass to the function the points where you want to calculate descriptors. I pass corners points.