Boost CVPR
Boost based Computer Vision and Pattern Recognition Library
Boostcvpr is a collection of C++
classes, functions and templates that implement many state-of-the-art popular
algorithms of Image Processing, Pattern Recognition and Computer Vision. It is
based on the famous Boost C++ library (http://www.boost.org/).
The key feature that distinguished Boostcvpr from other related libraries like OpenCV is that it is a pure C++ library and takes full advantages of C++ templates and Boost generic programming. It does not rely on external numerical libraries.
Boostcvpr uses boost::multi_array<> to contain images and matrices, boost::graph <> to represent graph, and boost::lambda and boost::mpl to build compile-time algorithms. This will make Boostcvpr very flexible and versatile.
The algorithms in Boostcvpr can be roughly divided into four categories:
*Basic algorithms, such as eigen solver, graph manipulation, fitting and interpolation.
*Image processing, such as filtering, histogram, edge detection.
*Pattern recognition and statistical learning, such as PCA, ICA, LDA, meanshift, SVM.
*Computer vision such as optical flow, kalman filter, image segmentation, object tracking.
The audience of Boostcvpr is researchers, students and engineers working in artificial intelligence, machine learning and computer vision.
The key feature that distinguished Boostcvpr from other related libraries like OpenCV is that it is a pure C++ library and takes full advantages of C++ templates and Boost generic programming. It does not rely on external numerical libraries.
Boostcvpr uses boost::multi_array<> to contain images and matrices, boost::graph <> to represent graph, and boost::lambda and boost::mpl to build compile-time algorithms. This will make Boostcvpr very flexible and versatile.
The algorithms in Boostcvpr can be roughly divided into four categories:
*Basic algorithms, such as eigen solver, graph manipulation, fitting and interpolation.
*Image processing, such as filtering, histogram, edge detection.
*Pattern recognition and statistical learning, such as PCA, ICA, LDA, meanshift, SVM.
*Computer vision such as optical flow, kalman filter, image segmentation, object tracking.
The audience of Boostcvpr is researchers, students and engineers working in artificial intelligence, machine learning and computer vision.