Pattern Recognition

What is Pattern Recognition?

 is the ability to detect arrangements of characteristics or data that yield information about a given system or data set. Pattern recognition is essential to many overlapping areas of IT, including big data analytics, biometric identification, security, and artificial intelligence (AI).

It is the ability to detect arrangements of characteristics or data that yield information about a given system or data set. Predictive analytics in data science work can make use of pattern recognition algorithms to isolate statistically probable movements of time series data into the future. In a technological context, a pattern might be recurring sequences of data over time that can be used to predict trends, particular configurations of features in images that identify objects, frequent combinations of words and phrases for natural language processing (NLP), or particular clusters of behavior on a network that could indicate an attack — among almost endless other possibilities. In IT, it is a branch of Machine Learning that emphasizes the recognition of data patterns or data regularities in each scenario. It involves classification and cluster of patterns.

Features of Pattern Recognition

  1. Completely rely on data and derives any outcome or model from data itself
  2. The system should recognize a familiar pattern quickly and accurate
  3. Recognize and classify unfamiliar objects very quickly
  4. Accurately recognize shapes and objects from different angles
  5. Identify patterns and objects even when partly hidden
  6. Recognize patterns quickly with ease, and with automaticity
  7. It always learns from data

Training and Learning Models

Training and Learning is the building block model of Pattern Recognition. Learning is a phenomenon through which a system gets trained and becomes adaptable to give results in an accurate manner. Learning is the most important phase as to how well the system performs on the data provided to the system depends on which algorithms used on the data.

 Training

The training set is used to build a model. It consists of a set of images that are used to train the system. Training rules and algorithms used to give relevant information on how to associate input data with output decisions. The system is trained by applying these algorithms on the dataset, all the relevant information is extracted from the data, and results are obtained. Generally, 80-85% of the data of the dataset is taken for training data.

 Testing

Testing data is used to test the system. It is the set of data that is used to verify whether the system is producing the correct output after being trained or not. Generally, 20% of the data of the dataset is used for testing. Testing data is used to measure the accuracy of the system.

Applications of Pattern Recognition

Computer visionPattern recognition is used to extract meaningful features from given image/video samples and is used in computer vision for various applications like biological and biomedical imaging.

Image processing, segmentation, and analysis: It’s been used to give human recognition intelligence to a machine which is required in image processing.

Pattern recognition is used in Terrorist Detection Credit Fraud Detection Credit Applications

Fingerprint identification: The fingerprint recognition technique is a dominant technology in the biometric market. Several recognition methods have been used to perform fingerprint matching out of which pattern recognition approaches are widely used.

Seismic analysis: The pattern recognition approach is used for the discovery, imaging, and interpretation of temporal patterns in seismic array recordings. Statistical pattern recognition is implemented and used in different types of seismic analysis models.

Radar signal analysis: Pattern recognition and signal processing methods are used in various applications of radar signal classifications like AP mine detection and identification.

Speech recognition: The greatest success in speech recognition has been obtained using pattern recognition paradigms. It is used in various algorithms of speech recognition which tries to avoid the problems of using a phoneme level of description and treats larger units such as words as a pattern.

Pattern Recognition Advantages

  1. Can interpret DNA Sequences
  2. IT has extensive application in astronomy, medicine, robotics, and remote sensing by satellites
  3. May solves classification problems
  4. Can solve the problem of fake biometric detection
  5. It is useful for cloth pattern recognition for visually impaired blind people
  6. It helps in forensic Lab

Conclusion

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