The Open Computer Vision Library is a collection of algorithms and sample code for various computer vision problems. The library is compatible with IPL (Intel Image Processing Library) and utilizes Intel Integrated Performance Primitives for better performance.
|Tags||Software Development Libraries Python Modules Scientific/Engineering Image Recognition|
|Operating Systems||Unix Windows Windows Windows|
|Implementation||C Python C++|
Release Notes: Full-featured Java desktop bindings were added, compatible with any JVM. Numerous improvements, new functionality, and optimizations were made for the CUDA gpu module. The OpenCL-based hardware acceleration (ocl) module is now mature, with various fixes and enhancements. The CLAHE (adaptive histogram equalization) algorithm was implemented, in both CPU and GPU-accelerated versions. A new video super-resolution module was added. Many bugfixes were made.
Release Notes: A universal "parallel_for" implementation was added for various backends. Many existing parallel OpenCV algorithms were converted to the new primitive. Android support was improved. iOS6 and iPhone5 compatibility was improved, with a new threading mechanism. New Python samples were added. Numerous improvements and new functionality additions were made in the GPU module. Bugs were fixed.
Release Notes: Various performance optimizations have been made. GPU support has been enhanced with new Optical Flow algorithms, new feature detectors and descriptors, and overall GPU module enhancements. It now supports CUDA 4.1 and CUDA 4.2, and can be compiled with CUDA 5.0 preview. An enormous amount of new functionality has been added. Addition of new modules has been made much easier.
Release Notes: Many functions and methods now take InputArray/OutputArray instead of "cv::Mat" references. LAPACK was replaced by an implementation of Jacobi SVD, decreasing footprint and compile time, and improving performance and accuracy. New BRIEF and ORB feature descriptors were added. A new calibration pattern "circles grid" was added. A new experimental variational stereo correspondence algorithm StereoVar was added. Support for Ximea cameras was added. Python modules were enhanced. Various other enhancements were made. Documentation was improved. Many bugs were fixed.
Release Notes: The library was reorganized into 12 smaller instead of 4 larger modules, while maintaining backward compatibility. An experimental CUDA acceleration module was added. Many improvements and enhancements were made. Bugs were fixed.