Development Group
EXPERTISE

PATTERN RECOGNITION

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.

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

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.

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.