What is the main research content of computer vision?
The main research content of computer vision is: using cameras and computers to replace human eyes for machine vision such as target recognition, tracking and measurement, and further graphics processing, making computer processing more suitable for human eye observation or Images sent to the instrument for detection.
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Computer vision is the study of how to make machines "see" Science, to put it further, refers to the use of cameras and computers to replace human eyes in machine vision such as target identification, tracking and measurement, and further graphic processing to make computer processing more suitable for human eye observation or transmission to instruments for detection. image.
As a scientific discipline, computer vision studies related theories and technologies, trying to build artificial intelligence systems that can obtain "information" from images or multi-dimensional data. The information referred to here refers to information defined by Shannon that can be used to help make a "decision".
Because perception can be seen as extracting information from sensory signals, computer vision can also be seen as the science that studies how to make artificial systems "perceive" from images or multi-dimensional data.
Computer vision is a simulation of biological vision using computers and related equipment. Its main task is to obtain three-dimensional information of the corresponding scene by processing the collected pictures or videos, just like what humans and many other creatures do every day.
Related
There are many disciplines whose research goals are similar to or related to computer vision. These disciplines include image processing, pattern recognition or image recognition, scene analysis, image understanding, etc. Computer vision includes image processing and pattern recognition. In addition, it also includes the description of spatial shapes, geometric modeling, and cognitive processes. Achieving image understanding is the ultimate goal of computer vision.
1. Image processing
Image processing technology converts the input image into another image with desired characteristics. For example, the output image can be processed to have a higher signal-to-noise ratio, or the details of the image can be highlighted through enhancement processing to facilitate operator inspection. Image processing technology is often used for preprocessing and feature extraction in computer vision research.
2. Pattern recognition
Pattern recognition technology divides images into predetermined categories based on the statistical characteristics or structural information extracted from the image. For example, text recognition or fingerprint recognition. In computer vision, pattern recognition technology is often used to identify and classify certain parts of an image, such as segmented areas.
3. Image understanding
Given an image, the image understanding program not only describes the image itself, but also describes and interprets the scenery represented by the image in order to make decisions about the content represented by the image. Decide. In the early days of artificial intelligence vision research, the term scene analysis was often used to emphasize the difference between two-dimensional images and three-dimensional scenes. In addition to complex image processing, image understanding also requires knowledge about the physical laws of scene imaging and knowledge related to the content of the scene.
When building a computer vision system, relevant technologies in the above disciplines need to be used, but the content of computer vision research is broader than these disciplines. The study of computer vision is closely related to the study of human vision. In order to achieve the goal of establishing a general computer vision system similar to the human visual system, it is necessary to establish a computer theory of human vision.
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