Originally posted on 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
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Originally posted on Gil's CV blog:
This fourth post in our series about binary descriptors that will talk about the BRISK descriptor . We had an introduction to patch descriptors, an introduction to binary descriptors and posts about the BRIEF  and the ORB  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.
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Originally posted on charmie11:
this is a note about free C++ libraries and MatLab toolboxes for Active Appearance Model.
1. C++ libraries:
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.
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.
is deformable face tracking library based on AAM.
Looking at the author’s website, the library must work quite nice.
It requires OpenCV.
is a C++ implementation of AAM.
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Originally posted on A GATEWAY TO ROBOTIC VISION:
AUTHORS:ABHISHEK KUMAR ANNAMRAJU,AKASH DEEP SINGH,ADHESH SHRIVASTAVA
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.1
3)Compiling and Executing:-
Open a terminal and change the directory to where the file is present and type
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Originally posted on Stackable:
OpenCV is great for all kinds of computer vision tasks. Many of these can run in a fully automated fashion, where parameters for the CV algorithms are provided by the user before the program begins or can be determined algorithmically at run time. Some, however, cannot.
For example, for my current project I am trying to find the optimal settings for OpenCV’s implementation of the stereo block matching algorithm. This requires computing disparity pictures, examining them visually, and deciding whether the parameters let the block matcher perform well or not. This is fairly subjective work, and it’s really annoying if you have to restart your program in order to see results with other settings or, if you’re using e.g. the Python interpreter, retype your arguments and display the window again. Of course, you could run a loop over all possible parameter combinations, but that makes it hard to experiment.
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Originally posted on Rodrigo's Blog:
It’s usually very time-consuming to start a new project because you have to manually add the libs and include directories. And when there’s a lot of them. It’s a headache.
Fortunately CMake has made this a really easy step. You only create the CMakeLists.txt file where your project properties reside and CMake does the rest.
In this tutorial I’ll try to explain how to get up and running in no time with OpenCV and Visual Studio 2010.
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