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What are the pattern recognition technology_application of pattern recognition technology

July 05, 2021

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What is pattern recognition technology

Pattern recognition technology is the basic technology of artificial intelligence. The 21st century is the century of intelligence, informationization, computing, and networking. In this century characterized by digital computing, pattern recognition technology as a basic subject of artificial intelligence technology must There will be huge room for development.


Pattern recognition has developed since the 1920s. A common perception is that there is no single model and single technology for solving all pattern recognition problems. What we have is just a tool bag, and what we need to do is to combine the specifics. The question combines statistical and syntactic recognition, combines statistical pattern recognition or syntactic pattern recognition with heuristic search in artificial intelligence, combines statistical pattern recognition or syntactic pattern recognition with machine learning of support vector machines, and combines artificial The neural network is combined with various existing technologies, expert systems in artificial intelligence, and uncertain reasoning methods to gain an in-depth grasp of the effectiveness and possibilities of various tools, learn from each other's strengths, and create a new situation in the application of pattern recognition.


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What are the pattern recognition technologies

Since the development of pattern recognition in the 1920s, there is a general perception that there is no single model and single technology for solving recognition problems that are applicable to all pattern recognition problems. What we have now is just a tool bag. What we need to do is Combining specific problems, combining statistical and syntactic recognition, combining statistical pattern recognition or syntactic pattern recognition with heuristic search in artificial intelligence, combining statistical pattern recognition or syntactic pattern recognition with machine learning of support vector machines, Combine artificial neuron networks with various existing technologies, expert systems in artificial intelligence, and uncertain reasoning methods to gain a thorough understanding of the effectiveness and possibilities of various tools, learn from each other's strengths, and create a new situation in pattern recognition applications .


1. Voice recognition technology


Voice recognition technology is gradually becoming the key technology of man-machine interface in information technology, and the application of voice technology has become a competitive emerging high-tech industry. The market forecast of the China Internet Center: In the next 5 years, the Chinese voice technology field will have a market capacity of more than 40 billion yuan, and then it will grow at a rate of more than 30 per year.


2. Biometric authentication technology


Biometric authentication technology is the most concerned security authentication technology in this century, and its development is the general trend. People are willing to forget all passwords, throw away all magnetic cards, and use their uniqueness to identify and keep secret. International Data Corporation (IDC) predicts that biometrics, which is the basic core technology of mobile e-commerce, as the inevitable development direction in the future, will reach a market size of 10 billion U.S. dollars in the next 10 years.


3. Voiceprint recognition


In recent years, in the field of biometric technology, voiceprint recognition technology has attracted worldwide attention due to its unique advantages such as convenience, economy, and accuracy, and has increasingly become an important and popular security verification method in people's daily life and work. Moreover, the speech recognition method that uses genetic algorithms to train the continuous hidden Markov model has become the mainstream technology of speech recognition. This method has a faster recognition speed during speech recognition and a higher recognition rate.


4. Fingerprint recognition


The uneven skin on the inner surface of our palms, fingers, feet, and toes will form a variety of patterns. The patterns, breakpoints and intersections of these skins are different and unique. Relying on this uniqueness, a person can be matched with his fingerprints, and his true identity can be verified by comparing his fingerprints with pre-saved fingerprints. Generally fingerprints are divided into several major categories: leftloop, rightloop, twinloop, whorl, arch and tentedarch; in this way, each person's fingerprints can be classified and retrieved separately. Fingerprint recognition can basically be divided into several major steps: preprocessing, feature selection and pattern classification.


5. Digital watermarking technology


Digital watermarking technology, which has only been developed internationally since the 1990s, is the most promising and advantageous digital media copyright protection technology. IDC predicts that the global market capacity of digital watermarking technology will exceed US$8 billion in the next five years.


Application of pattern recognition technology

Pattern recognition can be used in text and speech recognition, remote sensing, and medical diagnosis.


①Text recognition


Chinese characters have a history of thousands of years, and they are also the most frequently used characters in the world. They have indelibly contributed to the formation and development of the splendid culture of the Chinese nation. Therefore, with the increasing popularity of information technology and computer technology, how to input text into computers conveniently and quickly has become an important bottleneck that affects the efficiency of human-computer interfaces, and it is also related to whether computers can truly be widely used in our lives. At present, Chinese character input is mainly divided into two types: manual keyboard input and automatic machine recognition input. Among them, manual typing is slow and labor-intensive; automatic input is divided into Chinese character recognition input and voice recognition input. In terms of the difficulty of recognition technology, the difficulty of handwriting recognition is higher than that of print recognition, and in handwriting recognition, the difficulty of offline handwriting far exceeds that of on-line handwriting recognition. So far, in addition to the practical application of offline handwritten digit recognition, the offline handwritten recognition of Chinese characters and other characters is still in the laboratory stage.


②Voice recognition


The fields of speech recognition technology include: signal processing, pattern recognition, probability theory and information theory, sound mechanism and hearing mechanism, artificial intelligence and so on. In recent years, in the field of biometric technology, voiceprint recognition technology has attracted worldwide attention due to its unique advantages such as convenience, economy, and accuracy, and has increasingly become an important and popular security verification method in people's daily life and work. Moreover, the speech recognition method that uses genetic algorithms to train the continuous hidden Markov model has become the mainstream technology of speech recognition. This method has a faster recognition speed during speech recognition and a higher recognition rate. 2.3 Fingerprint recognition


The uneven skin on the inner surface of our palms, fingers, feet, and toes will form a variety of patterns. The patterns, breakpoints and intersections of these skins are different and unique. Relying on this uniqueness, a person can be matched with his fingerprints, and his true identity can be verified by comparing his fingerprints with pre-saved fingerprints. General fingerprints are divided into the following major categories: leftloop, rightloop, twinloop, whorl, arch and tentedarch, so that each person's fingerprints can be classified and retrieved separately. Fingerprint recognition can basically be divided into several major steps: preprocessing, feature selection and pattern classification.


③Remote sensing


Remote sensing image recognition has been widely used in crop yield estimation, resource prospecting, weather forecasting and military reconnaissance.


④Medical diagnosis


Pattern recognition has achieved results in cancer cell detection, X-ray photo analysis, blood tests, chromosome analysis, electrocardiogram diagnosis, and EEG diagnosis.