Computer vision

Computer vision is a field of computer science that develops techniques for computers to interpret visual data from the real world. Specifically, it involves the ability of a computer to identify objects and people in digital images and videos, and interpret their visual information to build a model of the physical environment. It is closely related to Robotics and Machine Learning, which are both branches of Artificial Intelligence.

Computer vision is used in applications such as image recognition, motion detection, facial recognition, and object classification. It is also used for navigation, guidance, and surveillance in autonomous cars, robots, and drones. Computer vision systems also have applications in medical imaging, military surveillance, industrial inspection, and surveillance.

Computer vision systems can be divided into two main categories: supervised and unsupervised learning. In supervised learning, a model is trained by a set of labeled data. Once a model is trained, it is used to predict results of incoming data. In unsupervised learning, no labeled data is used so the algorithm must discover patterns on its own.

The two main components of computer vision systems are the digital image capture devices and the algorithmic approaches. Image capture devices are used to acquire digital images, such as RGB cameras, high-speed cameras, and 3D scanners. Algorithmic approaches are used to post-process the images and extract meaningful information. These algorithms are generally based on deep learning and machine learning methods, such as Convolutional Neural Networks (CNNs).

Overall, computer vision is an exciting and rapidly evolving field of computer science with far-reaching implications. It is used to improve the accuracy and speed of decision-making systems, reduce costs in manufacturing processes, and improve the performance of autonomous vehicles. These and many other applications promise to be a driver of innovation in the coming decades.