Computer vision applications are:
(1) controlling a process, e.g., an industrial robot;
(2) navigating, e.g., through an autonomous vehicle or a mobile robot;
(3) detecting an event, e.g., for video surveillance and head counting;
(4) organizing information, e.g., for indexing of images and sequences of images in a databases;
(5) modeling objects or environments, such as, for example, a medical image analysis system or a terrain model;
(6) interacting with each other, such as, for example, when inputting to a device for computer-human interaction;
(7) automated detection, such as, for example, in manufacturing applications.
One of the most prominent application areas is medical computer vision and medical image processing. Information about the characteristics of the region is extracted from image data for the purpose of making medical diagnoses of patients. Typically, image data is in the form of microscope images, X-ray images, angiography images, ultrasound images and tomography images. An example of information that can be extracted from such image data is the detection of tumors, atherosclerosis or other malignant changes. It can also be the dimensions of organs, blood flow, and so on. This field of application is also supported by providing new information for medical research measurements for example, on the structure of the brain, or about the quality of medical treatments. Applications of computer vision in the medical field also include the enhancement of images that are interpreted by humans, such as ultrasound images or X-ray images, to reduce the effect of noise.
The second application area in which computer vision is used is in industry, sometimes referred to as machine vision, where information is extracted for the purpose of underpinning a manufacturing process. One example is quality control, where information or the final product is automatically detected in order to find defects. Another example is that of location and detail orientation measurements being picked up by a robot arm. Machine vision is also heavily used in the agricultural process of optical sorting of food from bulk materials, a process known as removal of unwanted items.
Extended information:
Computer vision, image processing, image analysis, robot vision and machine vision are disciplines that are closely related to each other. If you look through a textbook with these names, you will find that they all overlap for a significant portion of their technical and application areas. This suggests that the underlying theories of these disciplines are roughly the same, to the point where one might suspect that they are the same discipline under different names.
However, research institutes, journals, conferences, and companies tend to categorize themselves as being in one of these areas in particular, and a variety of characteristics have been proposed to differentiate between these disciplines. One method of differentiation is given below, although it cannot be said to be entirely accurate.
Computer vision is primarily concerned with three-dimensional scenes mapped onto single or multiple images, such as the reconstruction of a three-dimensional scene. The study of computer vision is largely directed to the content of the images.
The object of research in image processing and image analysis is mainly two-dimensional images, realizing image transformations, especially for pixel-level operations such as image contrast improvement, edge extraction, noise removal and geometric transformations such as image rotation. This characteristic suggests that the research content of both image processing and image analysis is not related to the specific content of the image.
Machine vision mainly refers to vision research in industrial areas, such as vision for autonomous robots, vision for inspection and measurement. This suggests that in this field through software hardware, image perception and control theory often get tightly integrated with image processing to realize efficient robot control or various real-time operations.
Pattern recognition uses a variety of methods to extract information from signals, primarily using statistical theory. One of the main directions in this field is the extraction of information from image data.
Another field is known as imaging technology. The initial research in this field focused on creating images, but sometimes it also involved image analysis and processing. Medical imaging, for example, encompasses a great deal of image analysis in the medical field.
For all of these fields, one possible course of events is that you work in a lab in computer vision, work on image processing, eventually solve problems in the field of machine vision, and then present your results at a conference on pattern recognition.
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