Technological disruption in object understanding can become an
effective instrument to address urgent computer vision tasks. One
of such paramount tasks is optimization of automated facial
recognition algorithms. Recent approaches utilizing head pose
and gaze estimation add up much to this subject matter.
Computer vision and image recognition have become the tablestakes
technologies that cater to the evolving business needs of
startups and incumbents across all industries. For now, both the
public and private sectors utilize computer vision algorithms and
applications for multiple purposes – to identify jaywalkers, track
shoppers, collect and analyze data on human biometrics and
demographics, to name a few.
Nowadays you can often hear of using OpenCV especially in projects related to computer vision. So you ask, what is OpenCV? How is it used? Hope you will find some answers below! So, OpenCV is a library which is mainly used for image processing projects. OpenCV is open-sourced and it’s cross platform, which makes it extremely convenient to use. It supports following frameworks: Caffe, TensorFlow, and Torch/PyTorch. It is written mainly in C++ (and runs primarily in C++ interface), however, it has bonds in Python, Java, and MATLAB. OpenCV works on different platforms, e.g. macOS, Windows, Linux, and some others. It also can be used for mobile applications development.Read more
Can one small sentence explain almost everything about computer vision? Definitely not! So, let’s take a bit deeper look at this new modern computer science field. Does it have any future? Is it really worth talking about?Read more