1. The design goal of OpenCV is to execute as fast as possible, mainly focusing on real-time applications.
2. One of the goals of OpenCV is to build an easy-to-use computer vision framework to help developers design more complex computer vision-related applications more easily. Because computer vision and machine learning are closely related, OpenCV also provides the MLL (Machine Learning Library) machine learning library.
3. OpenCV application areas: including stitching of satellite maps and electronic maps, alignment of scanned images, denoising of medical images, analysis of objects in images, security and intrusion detection systems, automatic surveillance and security systems, product quality inspection, camera calibration , unmanned aerial vehicles, unmanned vehicles, unmanned underwater robots, etc.
4. Computer Vision: Converting data from still images or video into a decision or a new way of expression.
5. Structure and content of OpenCV: The topic is divided into 5 modules. The CV module contains basic image processing functions and advanced computer vision algorithms. ML is a machine learning library that includes some statistics-based classification and clustering tools. HighGUI contains image and video input/output functions. CXCore contains some basic data structures and related functions of OpenCV.
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