Pattern Recognition

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Using pattern recognition for object detection, classification, and computer vision segmentation

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.

Supervised 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.

Face detection (left) and stop sign detection (right) using cascade classifiers.
Face detection (left) and stop sign detection (right) using cascade classifiers. See example and tutorial for details.
Detecting people using support vector machines (SVM) and HOG feature extraction.
Detecting people using support vector machines (SVM) and HOG feature extraction. See documentation for details.

Unsupervised Classification

The unsupervised classification method works by finding hidden structures in unlabeled data using segmentation or clustering techniques. Common unsupervised classification methods include:

  • K-means clustering
  • Gaussian mixture models
  • Hidden Markov models

In image processing and computer vision, unsupervised pattern recognition techniques are used for object detection and image segmentation.

Detecting moving objects by classifying image pixels in into foreground (white pixels) and background (black pixels) using Gaussian mixture models.
Detecting moving objects by classifying image pixels in into foreground (white pixels) and background (black pixels) using Gaussian mixture models. See example for details.
Color-based image segmentation using k-means clustering.
Color-based image segmentation using k-means clustering.

For details, see Computer Vision System Toolbox, Image Processing Toolbox, and Statistics Toolbox, which are used with MATLAB.

Examples and How To

Software Reference

See also: object detection, object recognition, image recognition, face recognition, feature extraction, object tracking, image segmentation, machine learning, pattern recognition videos