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. 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. The unsupervised classification method works by finding hidden structures in unlabelled data using segmentation or clustering techniques. Common unsupervised classification methods include: K-means clustering, Gaussian mixture models, Hidden Markov models. The aim of the topic is to provide knowledge about the different methods in pattern recognition and how to choose the optimum method for a specific spatial problem.
Rob Lemmens: Please add these skills, which come from the removed concept '[AM10-4] Pattern recognition and matching':
Differentiate among machine learning, data mining and pattern recognition
Explain the outcome of an artificial intelligence analysis e.g., edge recognition, including a discussion of what the human did not see that the computer identified and vice versa
Explain the principles of pattern recognition
Apply a simple spatial mean filter to an image as a means of recognizing patterns
Construct an edge-recognition filter
Design a simple spatial mean filter
In progress (GI-N2K)