In the late 1960s, computer vision began at universities that were pioneering artificial intelligence. As a technological discipline, computer vision seeks to apply its theories and models for the construction of computer vision systems. The image data can take many forms, such as video sequences, views from multiple cameras, or multi-dimensional data from a medical scanner. It involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding.' As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images. 'Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. There are very few companies that provide a unified and distributed platform or an Operating System where computer vision applications can be easily deployed and managed.Ĭomputer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. Sub-domains of computer vision include scene reconstruction, object detection, event detection, video tracking, object recognition, 3D pose estimation, learning, indexing, motion estimation, visual servoing, 3D scene modeling, and image restoration.Īdopting computer vision technology might be painstaking for organizations as there is no single point solution for it. The technological discipline of computer vision seeks to apply its theories and models to the construction of computer vision systems. The image data can take many forms, such as video sequences, views from multiple cameras, multi-dimensional data from a 3D scanner, 3D point clouds from LiDaR sensors, or medical scanning devices. The scientific discipline of computer vision is concerned with the theory behind artificial systems that extract information from images. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory. Understanding in this context means the transformation of visual images (the input to the retina in the human analog) into descriptions of the world that make sense to thought processes and can elicit appropriate action. Computerized information extraction from imagesĬomputer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g.

Sonic generations extract models