In their blog post , they have mentioned that they use FREAK descriptor but they haven't mentioned which detector are they using to identify the keypoints . Does anybody know anything about it???
The version 5 of the ARToolkit works using SURF features.
ARToolkit 6, to be released soon, will use FREAK features, based on Harris corners.
Regards,
Daniel
Related
today I found this paper "Real-Time Line Detection Through an Improved Hough Transform Voting Scheme" from here http://www2.ic.uff.br/~laffernandes/projects/kht/. This one is published on 2007 so I just wonder if opencv is using it or not? If not, then why?
Thank you.
If you find a random computer vision paper on the internet, assume it's not implemented in openCV. This has mostly to do with limited time of the contributors of openCV.
I do see that they provide the source code (both in mathlab and C++), so it should not be that difficult to implement it in openCV.
Source code of this paper in C++, Matlab and Python.
Is this possible in OpenCV or other image processing library?
It should find 3 Shape1 and 2 Shape2. (Meaning it has to process scaling and rotation) And give a position.
I am kind of new to opencv and do not know which algorithm or functions to use to do this. Any help would be very appreciated especially any code. Would the 2 unconnected parts in Shape1 cause problems in detection?
You could try the Chamfer algorithm.
OpenCV has a CPP example in opencv/samples/cpp/chamfer.cpp.
Below is an older version I found via google:
https://code.ros.org/trac/opencv/browser/trunk/opencv/samples/cpp/chamfer.cpp?rev=4194
I was wondering If I can detect motion more reliably by using Feature Detectors (like SURF, ORB, etc...)... Will it work for human beings in a video stream?
SIFT flow may give you some ideas.
I've done a project in OpenCV which may be useful to you.
You can find it here
I want to create a photo and video manipulation app for the iPad. The app would effect the imagery in various ways (using canny edge detection or bilateral blur for instance).
I saw some very interesting examples of canny edge detection using OpenCV, but is OpenCV the right tool to be looking into if I want to create a graphics app like this?
If so can anyone recommend some good reading materials to get me started.
Thanks for reading!
Yes, you certainly can use OpenCV on iOS. You simply cross-compile the code and include it in your project. OpenCV can easily do what you describe, and much more.
O'Reilly has published a great book on OpenCV, which is probably the best way to get up to speed. It explains the methods and how to use them, with plenty of sample code and images.
Learning OpenCV, Gary Bradski, Adrian Kaehler, O'Reilly 2012
There are a few sample projects around:
Sample OpenCV on iOS project
There are also numerous build scripts etc but note that they are probably not the latest version (2.4).
I was trying to build a basic Face Recognition system (PCA-Eigenfaces) using OpenCV 2.2 (from Willow Garage). I understand from many of the previous posts on Face Recognition that there is no standard open source library that can provide all the face recognition for you.
Instead, I would like to know if someone has used the functions(and integrated them):
icvCalcCovarMatrixEx_8u32fR
icvCalcEigenObjects_8u32fR
icvEigenProjection_8u32fR
et.al in the eigenobjects.cpp to form a face recognition system, because the functions seem to provide much of the required functionality along with cvSvd?
I am having a tough time trying to understand to do so since I am new to OpenCV.
Update: OpenCV 2.4.2 now comes with the very new cv::FaceRecognizer. Please see the very detailed documentation at:
http://docs.opencv.org/trunk/tutorial_face_main.html
I worked on a project with CV to recognize facial features. Most people don't understand the difference between Biometrics and Facial Recognition. There is a huge difference based on the fact that Biometrics is mainly based on histogram density matching while Facial Recognition implements this and vector support based on feature recognition from density. Check out the following link. This is the library you want to use if you are pursuing CV and Facial Recognition: www.betaface.com . Oleksander is awesome and based out of Germany, but he answers questions which is nice.
With OpenCV it's easy to get started with face detection. It comes with some predefined sets for feature detection, including face detection.
You might already know this one: OpenCV Wiki, FaceDetection
The important functions in this example are cvLoad and cvHaarDetectObjects. The first one loads the classifier and the second one applies it to an image.
The standard classifiers work pretty well. Of course you can train your own classifiers, if the standard ones don't fit your purpose.
As you said there are a lot of algorithms for face detection. Some of them might provide better results, but OpenCV is definitively a good start.