{"id":54815,"date":"2020-01-05T00:00:00","date_gmt":"2020-01-04T21:00:00","guid":{"rendered":"https:\/\/prohoster.info\/blog\/blog_prohoster\/vypusk-biblioteki-kompyuternogo-zreniya-opencv-4-2"},"modified":"2020-02-18T14:02:52","modified_gmt":"2020-02-18T11:02:52","slug":"vypusk-biblioteki-kompyuternogo-zreniya-opencv-4-2","status":"publish","type":"post","link":"https:\/\/prohoster.info\/en\/blog\/news\/vypusk-biblioteki-kompyuternogo-zreniya-opencv-4-2","title":{"rendered":"Release of the OpenCV 4.2 computer vision library","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Took place<\/a><\/noindex> release of the open-source library OpenCV 4.2<\/a><\/noindex> (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 distributed<\/a><\/noindex> is licensed under BSD. Bindings are provided for various programming languages, including Python, MATLAB, and Java.<\/p>\n

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.<\/p>\n

In new<\/a><\/noindex> release<\/a><\/noindex>:<\/p>\n