Pattern Recognition (science)
Pattern recognition is a scientific discipline focused on identifying patterns within various types of data, including visual and auditory information. Its primary goal is to develop machines that can autonomously recognize patterns to perform specific tasks or make decisions. The field integrates knowledge from statistical analysis, engineering, and applied mathematics, emerging prominently in the 1970s and gaining traction with the development of computer systems in the late 20th century. Pattern recognition plays a crucial role in numerous applications, such as facial recognition technology, which analyzes visual data to confirm identities, and computer-aided diagnosis (CAD) systems in healthcare that enhance the accuracy of disease evaluations.
The process involves obtaining data through sensors, translating it into a digital format, and using algorithms to categorize the information based on predetermined rules. As machines process data, they can learn and adapt, improving their accuracy over time. This technology is also increasingly prevalent in everyday devices, such as smartphones and virtual assistants. Looking ahead, pattern recognition continues to evolve, with potential advancements in robotics, space exploration, and neural network development, reflecting its growing significance across various sectors in society.
Pattern Recognition (science)
Summary
Pattern recognition is a branch of science concerned with identifying patterns within any type of data, from mathematical models to visual and auditory information. Applied pattern recognition aims to create machines capable of independently identifying and using patterns to perform tasks or make decisions. The field involves cooperation between statistical analysis, mechanical and electrical engineering, and applied mathematics. Pattern recognition technology emerged in the 1970s from work in advanced theoretical mathematics. The first pattern recognition systems for computers were developed in the 1980s and early 1990s. Pattern recognition technology is found in complex analyses of systems like economic markets and physiology and in everyday electronics like personal computers.
Definition and Basic Principles
Pattern recognition is a field of science concerned with creating machines and programs to categorize objects or bits of data into various classes. A pattern is broadly defined as a set of objects or parts with a relationship. Recognizing patterns requires the ability to distinguish individual parts, identify the relationship between the parts, and remember the pattern for future applications. Pattern recognition is closely linked to machine learning and artificial intelligence, which are concerned with creating machines capable of learning new information and using it to make decisions. Pattern recognition is one of the tools used by learning machines to solve problems.
One example of a pattern recognition machine is a computer or smartphone capable of facial recognition. The computer first evaluates the face using a visual sensor and then divides the face into parts, including the eyes, lips, and nose. Next, the system assesses the relationship between individual parts, such as the distance between the eyes, and notes features such as the length of the lips. Once the machine has evaluated an individual's face, it can store the data in memory and later compare the information against other facial scans. Facial recognition is often used to confirm identity in security applications or as a password to log into computers and cell phones.
Background and History
The earliest work on pattern recognition came from theoretical statistics. Early research concentrated on creating algorithms that would later be used to control pattern recognition machines. Pattern recognition became a distinct branch of mathematics and statistics when Engineer Jean-Claude Simon began publishing papers on optical pattern recognition in the early 1970s. Several researchers across the globe followed his work. The first international conference on pattern recognition was held in 1974, followed by the creation of the International Association for Pattern Recognition in 1978.
The United States spent $80 million in 1985 to develop visual and speech recognition systems. During the 1990s, pattern recognition systems became common in household electronics and were used for industrial, military, and economic applications. By the twenty-first century, pattern recognition had become a robust field with applications ranging from consumer electronics to neuroscience. The field continues to evolve with developments in artificial intelligence and computer engineering.
How It Works
Pattern recognition systems are based on algorithms or sets of equations that govern how a machine performs certain tasks. There are two branches of pattern recognition research—developing algorithms for pattern recognition programs and designing machines that use pattern recognition to perform a function.
Obtaining Data. The first step in pattern recognition is to obtain data from the environment. Data can be provided by an operator or obtained through various sensory systems. Some machines use optical sensors to evaluate visual data, and others use chemical receptors to detect molecules or auditory sensors to evaluate sound waves.
Most pattern recognition computers focus on evaluating data according to a few simple rules. For example, a visual computer may be programmed to recognize only red objects and to ignore objects of any other color, or it may be programmed to look only at objects larger than a target length.
Translating Data. Once a machine has intercepted data, the information must be translated into a digital format so that it can be manipulated by the computer's processor. Any type of data can be encoded as digital information, from spatial relationships and geometric patterns to musical notes.
A character recognition program is programmed to recognize symbols according to their spatial geometry. In other words, such a program can distinguish the letter A from the letter F based on the unique organization of lines and spaces. As the machine identifies characters, these characters are encoded as digital signals. A user can then use a computer interface like a keyboard to manipulate the digital signals and create new patterns.
A character recognition system familiar to many computer users is the spelling assistant found on most word-processing programs. The spelling assistant recognizes patterns of letters as words and can therefore compare each word against a preprogrammed list of words. If a word is not recognized, the program looks for a word similar to the one typed by the user and suggests a replacement.
Memory and Repeated Patterns. In addition to recognizing patterns, machines must also use patterns in problem-solving. For example, the spelling assistant program allows the computer to compare patterns programmed into its memory against input given by a user. Word processors can also learn to identify new words, which become part of the machine's permanent memory.
Advanced pattern recognition machines must be able to learn without direct input from a user or engineer. Certain learning robots, for instance, are programmed with the capability to change their own programming according to experience. Machines can get faster and more accurate with repeated exposure to similar patterns.
Applications and Products
Computer-Aided Diagnosis. Pattern recognition technology is used by hospitals around the world in the development of computer-aided diagnosis (CAD) systems. CAD is a field of research that uses computer analysis to more accurately diagnose disease. CAD research is usually conducted by radiologists and also involves participation from computer and electrical engineers. CAD systems can be used to evaluate the results taken from various imaging techniques, including radiography, computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound. Radiologists can use CAD systems to evaluate disorders affecting any body system, including the pulmonary, cardiac, neurologic, and gastrointestinal systems.
At the University of Chicago Medical Center, the radiology department has obtained more than seventy patents for CAD systems and related technology. Among other projects, specialists use CAD systems to evaluate potential tumors and to detect breast cancer.
Speech Recognition Technology. Some pattern recognition systems allow machines to recognize and respond to speech patterns. Speech recognition computers function by recording and analyzing sound waves; individual speech patterns are unique, and the computer can use a recorded speech pattern for comparison. Speech recognition technology can help create security systems in which an individual's speech pattern is used as a passkey to gain access to private information. Speech recognition programs are also used to create dictation machines that translate speech into written documents. In the 2010s and 2020s, speech recognition programs were integrated into virtual assistants like Apple's Siri and Amazon's Alexa, which play music, search the internet, and control various home electronics through voice commands.
Neural Networks. Many animals, including humans, use pattern recognition to navigate their environments. By examining the brain’s behavior when confronted with pattern recognition problems, engineers are attempting to design artificial neural networks, which are machines that emulate the behavior of the brain.
Biological neural networks have a complex, nonlinear structure, which makes them unpredictable and adaptable to new problems. Artificial neural networks are designed to mimic this nonlinear function by using sets of algorithms organized into artificial neurons that imitate biological neurons. The artificial network is designed to be adaptive, so that repeated exposure to similar problems creates strong connections among the artificial neurons—similar to memory in the human brain.
Artificial neural networks can be used for any pattern recognition application from economic analysis to fingerprint identification. They are also commonly used in machine learning. Many engineers have come to believe that artificial neural networks are the future of pattern recognition technology and will eventually replace the linear algorithms that have been used most frequently.
Military Applications. Pattern recognition is one of the most powerful tools in developing military technology, including surveillance and offensive equipment. For example, the Tomahawk cruise missile, sometimes called a smart bomb, is an application of pattern recognition used for offensive military applications. The missile uses a digital scene area matching correlation (DSAMC) system to guide the missile toward a specific target identified by the pilot. The missile is equipped with sensors, an onboard computer, and flight fins that can be used to adjust its trajectory. After the missile is fired, the DSAMC adjusts the missile's flight pattern by matching images from its visual sensors with the target image.
Careers and Course Work
The most direct route to achieve a career in pattern recognition would be to receive advanced training in electrical engineering. Professional statisticians, mathematicians, neurobiologists, and physicists also participate in pattern research. Alternatively, some medical professionals work with pattern recognition, most notably radiologists who participate in CAD research and development.
Texts and training materials in pattern recognition generally require a strong background in mathematics and statistics. Basic knowledge of statistical analysis is a prerequisite for the most basic college courses in statistical engineering. Those hoping to work at the forefront of pattern recognition research will also need experience with machine learning, artificial intelligence, artificial neural network design, and related areas.
Social Context and Future Prospects
Pattern recognition technology has become familiar to many consumers. Voice-activated telephones, fingerprint security and facial recognition for personal computers and cellphones, and character analysis in word-processing software are just a few of the many applications that affect daily life. Pattern recognition technologies in social media, email sorting, and online banking grew exponentially in the 2010s and 2020s. Examples include Meta (formerly Facebook) using facial recognition to tag users in pictures and banking applications using fingerprint logins. According to a 2019 report by Juniper Research, a digital technology marketing research company, facial recognition software was used in 96 million mobile devices in 2019. However, in 2021, Meta shut down its facial reccognition software citing security concerns. Advances in medicine, military technology, economic analysis, criminal justice, and advertising are further examples of how pattern recognition has come to shape the development of society.
Projects that may represent the future of pattern recognition technology include the development of autonomous robots, space exploration, and the evaluation of complex dynamics. In robotics, pattern recognition is used to create robots that can locate objects in their environment. It has also been integrated into robots that assist individuals with disabilities during rehabilitation and with some prosthetic devices. The National Aeronautics and Space Administration (NASA) has used pattern recognition to create probes capable of finding objects or sites of interest on alien landscapes. Combined with research on artificial neural networks, automated systems can make complex decisions based on the recognition of patterns.
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