SURF in OpenCV

Achu's TechBlog

Let us  now see what is SURF.

SURF Keypoints of my palm

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Hand detection through Haartraining: A hands-on approach

Giga thoughts ...

Detection of objects from images or from video is no trivial task. We need to use some type of machine learning algorithm and train it to detect features and also identify misses and false positives.  The haartraining algorithm does just this. It creates a series of haarclassifiers which ensure that non-features are quickly rejected as the object is identified.

This post will highlight the necessary steps required to build a haarclassifier for detection a hand or any object of interest. This post is sequel to my earlier post (OpenCV: Haartraining and all that jazz!) and has a lot more detail. In order to train the haarclassifier, it is suggested, that at least 1000 positive samples (images with the object of interest- hand in this case) and 2000 negative samples (any other image) is required.

As before for performing haartraining the following 3 steps have to be performed

1)     …

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A tutorial on binary descriptors – part 4 – The BRISK descriptor

Gil's CV blog

This fourth post in our series about binary descriptors that will talk about the BRISK descriptor [1]. We had an introduction to patch descriptors, an introduction to binary descriptors and posts about the BRIEF [2] and the ORB [3] descriptors.

We’ll start by showing the following figure that shows an example of using BRISK to match between real world images with viewpoint change. Green lines are valid matches, red circles are detected keypoints.

BRISK descriptor - example of matching points using BRISK BRISK descriptor – example of matching points using BRISK

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C++ library and MatLab toolbox for Active Appearance Model


this is a note about free C++ libraries and MatLab toolboxes for Active Appearance Model.

1. C++ libraries:

  1. DeMoLib
    implements several AAM fitting methods.
    Difference from other libraries is that DeMoLib provides several AAM fitting methods such as some Inverse Compositional algorithms and 2D+3D fitting method. This feature is fantastic.
    The library requires OpenCV, VxL, and CMake.
  2. CoDe library
    is implementation of a CVPR 2009 paper “On Compositional Image Alignment with an Application to Active Appearance Models.
    With the library, we can align an AAM to a target image from an initial starting position.
    Its documentation tells us that the library requires Blas, Lapack, Cmake, and MatLab. MatLab is probably for PCA based learning part of AAM.
  3. FaceTracker
    is deformable face tracking library based on AAM.
    Looking at the author’s website, the library must work quite nice.
    It requires OpenCV.
  4. AAM-API
    is a C++ implementation of AAM.

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Hi Friends,

Lets look into a way of opening image/video/camera with opencv

Reading an image:-

1)Create a new  c++ file (eg-open_image.cpp )

2)Code(copy the code in the file):-

Click once somewhere on the code and press ctrl+A to select whole code.You may not see the whole code so its better to copy the code and paste it in your favourite text editor and then go through it.

3)Compiling and Executing:-
Open a terminal and change the directory to where the file is present and type

a)chmod +x FILENAME.cpp

Note:FILENAME is the name given to the file,use the one you used

b)g++ -ggdb `pkg-config --cflags opencv` -o `basename FILENAME.cpp .cpp` FILENAME.cpp `pkg-config --libs opencv`

Reading a video/camera input:-

1)Create a new  c++ file (eg-open_image.cpp )

2)Code(copy the code in the file):-

 #include <highgui.h> CvCapture* capture = NULL; int main( int argc, char** argv )…

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