Revision as of 02:06, 1 February 2013

Introduction

         I find the mechanism of recognizing and sorting different pieces of information and then going on to make decisions or predictions based on the information collected to be very interesting both in humans and in electronics. In the series of essays i will be posting this semester, I will be writing about Pattern recognition and classification, with an emphasis on the relevance of probability to creating a pattern recognition system. Pattern recognition and classification is the act of taking in raw data and using a set of properties and features take an action on the data. As humans, our brains do this sort of classification everyday and every minute of our lives, from recognizing faces, sounds and voices. This cognitive task has been very crucial for our survival. Moving on, we seek to design models and systems that will be able to recognize and furthermore classify these patterns into different categories for further use. This enables us to design pattern recognition machines that have a variety of different applications from fingerprint identification, speech recognition to DNA sequence identification. Designing these systems are often very complicated due to the presence of many unknowns. To understand the problem of designing a system, we need to first understand the different components that make up a pattern recognition system.

Sensing

       The sensors in a system are what receives the data input, and they may vary depending on the purpose of the system. They are usually some form of transducers such as a camera or a microphone.

Segmentation

       After receiving the input data the different patterns need to be separated. Segmentation is one of the toughest problems of pattern recognition because a lot of the patterns tend to overlap and intermingle. For example, trying to recognize a pattern of the individual sound "s", in the two words "see", and "son" would prove difficult because the sound is pronounced differently in the two words, and using the same model to segment the 's" would not be accurate.

Feature Extraction and Classification

       Here, the goal is to characterize the data to be recognized by measurements that will give the same results for data in the same category and different results for data in different categories. This leads to finding distinguishing features that are invariant to any transformations of the data. The degree of classifying the input into different categories varies on the features of the data. While perfect classification is often impossible, an easier task is to find the probability of the data fitting one of the categories.

Post Processing

The post-processor uses the output of the classifier to decide on the recommended action on the data.

Below, Is a table that shows the various components in a flow chart manner