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Image Recognition: Key Application Fields of Artificial Intelligence

2022-07-20

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About image recognition

　　Image recognition is an important field of Artificial intelligence. It refers to the technology of using computers to process, analyze and understand images to identify targets and objects in various patterns. It is a practical application of deep learning algorithms.

　　In computer vision recognition systems, image content is usually described by image features. In fact, computer vision-based image retri can also be divided into three steps similar to text search engines: extracting features, building indexes, and querying.

　　The development of image recognition has gone through three stages: character recognition, digital image processing and recognition, and object recognition. The research on text recognition began in 1950. and it is generally used to recognize letters, numbers, and symbols, from printed text recognition to handwritten text recognition, and is widely used.

image recognition

The working process of image recognition

　　The human eye sees images as signals that are interpreted by the visual cortex of the brain. The result is a scene experience linked to objects and concepts stored in memory. Image recognition to simulate this process. A computer is a set of vectors (polygons with color annotations) or rasters (canvas of pixels with discrete color values) that "see" an image.

　　In neural network image recognition, the vector or raster encoding of an image is converted into a structure that describes physical objects and features. In a computer vision system, images are first simplified to extract the most important information, and then feature extraction and classification are performed to organize these structures. Ultimately, computer vision systems use classification or other algorithms to determine images or parts of images—which class they belong to, or how best to describe them.

You may want to know:The four main application areas of artificial intelligence

Application of Image Recognition Technology

　　1. Remote sensing image recognition: Aerial remote sensing and satellite remote sensing images are often processed with image recognition techniques to extract useful information. The technology is mainly used for topographic and geological exploration, forest, water conservancy, ocean, agriculture and other resource surveys, disaster prediction, environmental pollution monitoring, meteorological satellite cloud image processing, and ground military target recognition.

　　2. Applications in the field of communications: including image transmission, video telephony, video conferencing, etc.

　　3. Application in the military, public security criminal investigation, and other fields: image recognition technology is widely used in military and public security criminal investigation, such as reconnaissance, guidance, and warning systems of military targets; control and anti-camouflage of automatic fire extinguishers; on-site photos, fingerprints, Processing and identification of handwriting, seals, portraits, etc.; restoration and management of historical text and picture files, etc.

image recognition

　　4. Biomedical image recognition: Image recognition is widely used in modern medicine, and it has the characteristics of intuitive, non-invasive, safe, and convenient. Image recognition technology, such as CT (Computed Tomography) technology, is widely used in clinical diagnosis and pathological research.

　　Application in the field of machine vision: As an important sensory organ of intelligent robots, machine vision mainly understands and recognizes 3D images, and this technology is also one of the hot topics of research. The application fields of machine vision are also very wide, such as autonomous robots for military reconnaissance, and dangerous environments, and intelligent robots for postal, hospital, and home services. In addition, machine vision can also be used for workpiece identification and positioning in industrial production, automatic operation of space robots, etc.

Tag： Intelligent Hardware Technology Artificial Intelligence