release of the open-source library (Open Source Computer Vision Library), providing tools for image content processing and analysis. OpenCV offers over 2500 algorithms, both classic and reflecting the latest advancements in computer vision and machine learning systems. The library's code is written in C++ and is licensed under BSD. Bindings are provided for various programming languages, including Python, MATLAB, and Java.
The library can be used for object recognition in photographs and videos (for example, face and human figure recognition, text, etc.), tracking the movement of objects and cameras, classifying actions in videos, image transformation, extracting 3D models, creating 3D spaces from images from stereo cameras, generating high-quality images through the combination of lower-quality images, searching for objects in images that resemble a given set of elements, applying machine learning methods, placing markers, identifying common elements across different images, and automating defect removal, such as red-eye effect.
In :
- The DNN (Deep Neural Network) module, implementing machine learning algorithms based on neural networks, has added a backend for CUDA usage and implemented experimental support for the API ;
- Performance optimization of the code for stereo output (StereoBM/StereoSGBM), resizing, mask overlay, rotation, calculation of missing color components, and many other operations has been performed using SIMD instructions;
- A multithreaded implementation of the function ;
- The ability to extract video streams from media containers (demuxing) has been added using the videoio backend based on FFmpeg;
- An algorithm for fast frequency-selective reconstruction of damaged images has been added (Frequency Selective Reconstruction);
- A method has been added for interpolating typical unfilled areas;
- A method for normalizing deviations has been added ;
- In the G-API (opencv_gapi) module, which serves as the engine for efficient image processing using graph-based algorithms, support for more complex hybrid computer vision and deep machine learning algorithms has been implemented. Support for the Intel Inference Engine backend has been ensured. The execution model now supports video stream processing;
- Fixed (, ), which may potentially lead to the execution of attacker's code when processing unverified data in XML, YAML, and JSON formats. If a null character is encountered while parsing JSON, the value is wholly copied to the buffer, but without proper bounds checking.
Source: opennet.ru
