I want to get the connected component from an image with each components boundary pixels information,How can I use cvBlob library for the same..!!!
Though I have used cvFindContours forthis But I am not getting the desired result.
Thanks,
[This may help someone having similar problem]
If you provided a picture, you would have got the guidance on your problem. It's extremely hard to imagine what kind of picture you got.
The function findContours() may be finding all contours in your image, including those you do not need. You can always filter out certain contours as being too small, or too large. :)
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I need some help in generating a map.
Instead of taking one hundred screenshots of areas and trying to perfectly combine them in photoshop, I wonder if there is a script/software that can export me a big whole area with the same level of detail as I zoom-in. I assume it will be a ~50,000x50,000px image but i really don't mind.
The only thing I really tried is to take multiple screenshots and combine them but I need to work on a big canvas, program crashes or sometimes the whole project is messing up when arranging.
I'm asking a question for a school project. I am trying to play a video in a loop and change its speed according to the distance from the Kinect 1414. When you are far the video plays at normal speed, which then increases as you get closer.
I tried in different ways but sometimes either the video doesn't loop or it doesn't show, I can only hear the audio. Other solutions don't let me change the speed of the video. Do you know any way to have the distance of a person affect the video?
Thanks.
Stack Overflow isn't designed for general "how do I do this" type questions like this. It's for specific "I tried X, expected Y, but got Z instead" type questions. But I'll try to help in a general sense.
You need to break your problem down into smaller pieces and then take on those pieces one at a time. For example, can you start with a very basic sketch that just displays the distance from the Kinect? Don't worry about the video yet, just display the distance on the screen.
Separately from that, can you create another sketch that just shows a video playing in a loop? Work your way up from there: can you make it so its speed is based on a hard-coded value? How about on a value like mouseX?
Get those two basic sketches working perfectly by themselves before you start thinking about combining them. Then if you get stuck on one of those steps, you can post a MCVE along with a specific question (in a new question post), and we'll go from there. Good luck.
I would like to compare two mp4 files, does somebody has an idea?
Maybe by interposing the video spectrum?
Thanks.
I had an idea for this a while back. I never implemented it, but it went something like this:
Get a good video library to do the heavy lifting for you, I like Aforge.NET
Use the library to walk through the video and extract bitmap frames, get a few hundred
Fix the resolution to a single aspect ratio
Reduce the images to something low-res like 16x16 or 64x64, using a nearest neighbor approach. This will blur the image such that two similar images will reduce to the same
Gather a chunk of these images by relative video timestamp and hash them to further reduce the data
Compare said hashes
Again, I never implemented this, so I don't know if it works, but the thing it has going for it is that video is very complex. While comparing any given frame to another won't work, based on different formats, resolutions, etc., the odds of a series of reduced hashes being the same from two different videos seems very low. Thus, few false positives. Also it seems like it could also tell you if one span of video was contained in another.
If I get around to making something like this I'll circle back here and post about it.
I am working for a project at school regarding face detection, based on a technique described by Viola and Jones 2001/2004.
I've read that the OpenCV has an implementation of this algorithm, and it works very good.
I was wondering if you have any advices regarding what techniques (pre-processing) to apply to the images before testing the existence of a face (eg. histogram equalization) ?
I basically used the code from this sample program from the OpenCV page and it worked very well for my masters thesis project. If you get bad results or your lighting is strange you can try a histogram equalization.
with a friend I did something similar too for an university project, and especially on low resolution video sequences it really helped to upsample the frame, doubling its size. It was my friends' idea, who had previously taken an image processing class. Although equivalent, things like decreasing initial scan window size, horizontal and vertical steps didn't produce the same result. In other words it may be better to work on larger images with larger scan windows than on smaller with smaller scan windows. Don't know exactly why.
Bye ;-)
I know its too late. But do go through this site as well.
It speaks of the common pre-proccessing required for the images. Equalising the image, Editing irrelevant content etc
I am thinking about creating a database system for images where they are stored with compact signatures and then matched against a "query image" that could be a resized, cropped, brightened, rotated or a flipped version of the stored one. Note that I am not talking about image similarity algorithms but rather strictly about duplicate detection. This would make things a lot simpler. The system wouldn't care if two images have an elephant on them, it would only be important to detect if the two images are in fact the same image.
Histogram comparisons simply won't work for cropped query images. The only viable way to go I see is shape/edge detection. Images would first be somehow discretized, every pixel being converted to an 8-level grayscale for example. The discretized image will contain vast regions in the same colour which would help indicate shapes. These shapes then could be described with coefficients and their relative position could be remembered. Compact signatures would be produced out of that. This process will be carried out over each image being stored and over each query image when a comparison has to be performed. Does that sound like an efficient and realisable algorithm? To illustrate this idea:
removed dead ImageShack link
I know this is an immature research area, I have read Wikipedia on the subject and I would ask you to propose your ideas about such an algorithm.
SURF should do its job.
http://en.wikipedia.org/wiki/SURF
It is fast an robust, it is invariant on rotations and scaling and also on blure and contrast/lightning (but not so strongly).
There is example of automatic panorama stitching.
Check article on SIFT first
http://en.wikipedia.org/wiki/Scale-invariant_feature_transform
If you want to do a feature detection driven model, you could perhaps take the singular value decomposition of the images (you'd probably have to do a SVD for each color) and use the first few columns of the U and V matrices along with the corresponding singular values to judge how similar the images are.
Very similar to the SVD method is one called principle component analysis which I think will be easier to use to compare between images. The PCA method is pretty close to just taking the SVD and getting rid of the singular values by factoring them into the U and V matrices. If you follow the PCA path, you might also want to look into correspondence analysis. By the way, the PCA method was a common method used in the Netflix Prize for extracting features.
How about converting this python codes to C back?
Check out tineye.com They have a good system that's always improving. I'm sure you can find research papers from them on the subject.
The article you might be referring to on Wikipedia on feature detection.
If you are running on Intel/AMD processor, you could use the Intel Integrated Performance Primitives to get access to a library of image processing functions. Or beyond that, there is the OpenCV project, again another library of image processing functions for you. The advantage of a using library is that you can try various algorithms, already implemented, to see what will work for your situation.