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Both form researchers in the learning community and from researches involved with the computer vision world, there is an increasing interest in the area of Learning in Computer Vision and Image Understanding. The model is characterized by its data-driven learning paradigms for solving real-world problems.
Vision is a process which produces, from images of the external world, a description that is useful to the viewer and not cluttered with irrelevant information. (Marr and Nishihara, 1978). Computer Vision is the science and technology of machines that see. It is concerned with the theory and technology for building artificial systems that obtain information from images or multi-dimensional data.
Computer vision in applied in controlling processes (robots, vehicles), detecting events (visual surveillance), organizing information (indexing databases of images/videos), modeling objects or environments (medical image analysis) and interaction (HCI).
An image is an array or matrix of values (picture elements = pixels) on a plane that describe the world from the point of view of the observer. The meaning of pixels depends on the sensors used for their acquisition.
Image processing is a method of converting an image into digital form and perform some operations on it, in order to get an enhanced image or to extract useful information from it. It is a type of signal dispensation where input is an image, like video frame or photograph and output, may be image or characteristics associated with that image. Image processing usually includes treating images as two-dimensional signals while applying already set signal processing methods to them.
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