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.
Related
Need to use c for a project and i saw this screenshot in a pdf which gave me the idea
http://i983.photobucket.com/albums/ae313/edmoney777/Screenshotfrom2013-11-10015540_zps3f09b5aa.png
It say's you can treat each pixel of an image as a graph node(or vertex i guess) so i was wondering how
i would do this using OpenCV and the CvGraph set of functions. Im trying to do this to learn about and how
to use graphs in computer vision and i think this would be a good starting point.
I know i can add a vetex to a graph with
int cvGraphAddVtx(CvGraph* graph, const CvGraphVtx* vtx=NULL, CvGraphVtx** inserted_vtx=NULL )
and the documentation says for the above functions vtx parameter
"Optional input argument used to initialize the added vertex (only user-defined fields beyond sizeof(CvGraphVtx) are copied)"
is this how i would represent a pixel as a graph vertex or am i barking up the wrong tree...I would love to learn more about
graphs so if someone could help me by maybe posting code, links, or good ol' fashioned advice...Id be grateful=)
http://vision.csd.uwo.ca/code has an implementation on Mulit-label optimization. GCoptimization.cpp file has a GCoptimizationGridGraph class, which I guess is what you need. I am not a C++ expert, so can't still figure out how it works. I am also looking for some simpler solution.
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'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.
I have a video which has got turn left,turn right etc marks on the roads.
I have to detect those signs.I am going ahead with template matching in which I am matching the edge detected outputs,But I am not getting satisfactory results,Is there any other way to detect it? Please help.
If you want a solution that is not too complicated but more robust than template matching, I suggest you'd go for Hough voting on SIFT descriptors. This method is provides some degree of robustness to various problems, including partial occlusion of the sign, illumination variations and deformations of the sign. In particular, the method is completely invariant to rotation and uniform scaling of the template object.
The basic idea of the algorithm is as follows:
a) extract SIFT features from the template and query images.
b) set an arbitrary reference point in the template image and calculate, for each keypoint in the template image, the vector from the keypoint to the reference point.
c) match keypoints from the template image to the query image.
d) cast a vote for each matched keypoint for all object locations in the query image that this keypoint agrees with. You do that using the vectors calculated in step (b) and the location, scale and orientation of the matched keypoints in the query image.
e) If the object is indeed located in the image, the votes map should have a strong local maximum at it's location.
f) Optionally, you can verify the detection by using template matching.
You can read more about that method on Wikipedia here or in the original paper (by D. Lowe) here.
Using SIFT or SURF. You can get the invariable descriptor with training you can determine if the vector that represent the road marks (turn left, right or stop) match with the new in the video.
You might try extracting features and training a classifier (linear discriminant, neural network, naive Bayes, etc.). There are many candidate features you might try, but I'd think that you wouldn't need anything too complicated, even if the edge detection is poor, assuming that isolation of the sign is good. Some features to consider are: horizontal and vertical projections (row and column totals) and simple statistics of edge pixels (mean, standard deviation, skewness, etc. For more feature ideas, see any of these books:
"Shape Classification and Analysis: Theory and Practice", by Costa and Cesar
"Algorithms for Image Processing and Computer Vision", by J. R. Parker
"Digital Image Processing", by Gonzalez and Woods
I have to make an application that recognizes inside an black and white image a piece of tetris given by the user. I read the image to analyze into an array.
How can I do something like this using C?
Assuming that you already loaded the images into arrays, what about using regular expressions?
You don't need exact shape matching but approximately, so why not give it a try!
Edit: I downloaded your doc file. You must identify a random pattern among random figures on a 2D array so regex isn't suitable for this problem, lets say that's the bad news. The good news is that your homework is not exactly image processing, and it's much easier.
It's your homework so I won't create the code for you but I can give you directions.
You need a routine that can create a new piece from the original pattern/piece rotated. (note: with piece I mean the 4x4 square - all the cells of it)
You need a routine that checks if a piece matches an area from the 2D image at position x,y - the matching area would have corners (x-2, y-2, x+1, y+1).
You search by checking every image position (x,y) for a match.
Since you must use parallelism you can create 4 threads and assign to each thread a different rotation to search.
You might not want to implement that from scratch (unless required, of course) ... I'd recommend looking for a suitable library. I've heard that OpenCV is good, but never done any work with machine vision myself so I haven't tested it.
Search for connected components (i.e. using depth-first search; you might want to avoid recursion if efficiency is an issue; use your own stack instead). The largest connected component should be your tetris piece. You can then further analyze it (using the shape, the size or some kind of border description)
Looking at the shapes given for tetris pieces in Wikipedia, called "I,J,L,O,S,T,Z", it seems that the ratios of the sides of the bounding box (easy to find given a binary image and C) reveal whether you have I (4:1) or O (1:1); the other shapes are 2:3.
To detect which of the remaining shapes you have (J,L,S,T, or Z), it looks like you could collect the length and position of the shape's edges that fall on the bounding box's edges. Thus, T would show 3 and 1 along the 3-sides, and 1 and 1 along the 2 sides. Keeping track of the positions helps distinguish J from L, S from Z.