Computer Vision is one of the most popular research areas in deep learning and computer vision algorithms are just part of it. Computer Vision is cross-disciplinary, including Computer Science (Graphics, Algorithms, Theory, Systems, Architecture), Mathematics (information retrieval, machine learning), Engineering (robots, speech, natural language processing, image processing), Physics (Optics), Biology (Neurology) and Psychology (Cognitive Science). Numerous scientists acknowledge that computer vision has paved the way for the growth of artificial intelligence.

For those of you who are learning programming or learning coding, and have an interest in the field of artificial intelligence, computer vision can be used as a reference to deepen the sub-field of artificial intelligence. Computer vision primarily studies how to help machines “see” the world, using cameras instead of the human eye to obtain image information. It uses deep learning algorithms to identify, track and reconstruct certain predefined targets in the image. Some of the functions of the human eye are realized by machines. The famous VR, AR, and 3D reconstructions are all part of the field of computer vision. The following are the main application areas of Computer Vision:

Face recognition
Autopilot
Security Monitoring
Medical imaging
Robot technology
3D structure reconstruction
VR / AR
Drones
Image-based search
Image recognition applications, especially face recognition commercially, are relatively mature and widely used. Especially static image recognition technology with a very high level of accuracy.

In addition, the direction of medical imaging is also driven by the popularity of deep learning and Computer Vision in recent years. It has become a director with great development potential in academia and industry. This field of research has spawned many fast-growing practical applications, such as:

– Image capture: Google Images uses content-based queries to find relevant images. The algorithm analyzes the content in the query image and provides results based on the most suitable content.
– Games and controls: The most successful gaming application product that uses stereo vision is: Microsoft Kinect.
– Surveillance: Surveillance cameras used to monitor suspicious behavior are in public places.
– Biometrics: Fingerprint, iris, and facial matching are still some of the common methods in the field of biometrics.
– Smart cars: Computer vision is still the primary source of information for detecting traffic signs, lights, and other visual features.