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Artificial Intelligence (AI) can be used to solve a wide range of problems, including those related to computer vision, such as image recognition, object detection, and medical imaging. In the present paper we show how to integrate OpenCV* (Open Source Computer Vision Library) with a neural network backend. In order to achieve this aim, we first explain how the video stream is manipulated using a Python* programming interface and we also provide guidelines on how to use it. Finally, we discuss a working example of an OpenCV application. OpenCV is one of the packages that ship with Intel® Distribution for Python* 2018.
Today, the possibilities of artificial intelligence (AI) are accessible to almost everyone. There are a number of artificial intelligence applications and many of them require the use of computer vision techniques. One of the most currently used libraries to help detection and matching, motion estimation, and tracking is OpenCV1. OpenCV is a library of programming functions mainly aimed at real-time computer vision. Originally developed by Intel, it was later supported by Willow Garage and is now maintained by Itseez. The library is cross-platform and free for use under the open-source BSD license.
Usually, the OpenCV …
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