Pattern recognition is the process of classifying input data into objects or classes based on key features. There are two classification methods in pattern recognition: supervised and unsupervised classification.
Pattern recognition has applications in computer vision, radar processing, speech recognition, and text classification.
The supervised classification of input data in the pattern recognition method uses supervised learning algorithms that create classifiers based on training data from different object classes. The classifier then accepts input data and assigns the appropriate object or class label.
In computer vision, supervised pattern recognition techniques are used for optical character recognition (OCR), face detection, face recognition, object detection, and object classification.
The unsupervised classification method works by finding hidden structures in unlabeled data using segmentation or clustering techniques. Common unsupervised classification methods include:
In image processing and computer vision, unsupervised pattern recognition techniques are used for object detection and image segmentation.
See also: Deep Learning, object detection, object recognition, image recognition, face recognition, feature extraction, object tracking, image segmentation, machine learning, pattern recognition videos, point cloud, deep learning
In this course, you’ll determine how to use unsupervised learning techniques to discover features in large data sets and supervised learning techniques to build predictive models.