30-03-2012, 01:02 PM
PATTERN RECOGNITION
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INTRODUCTION:
"Pattern recognition is the research area that studies the operation and design of systems that recognize patterns in data. It encloses subdisciplines like discriminant analysis, feature extraction, error estimation, cluster analysis (together sometimes called statistical pattern recognition), grammatical inference and parsing (sometimes called syntactical pattern recognition). Important application areas are image analysis, character recognition, speech analysis, man and machine diagnostics, person identification and industrial inspection."
Pattern recognition is a field within the area of machine learning. Alternatively, it can be defined as “the act of taking in raw data and taking an action based on the category of the data”. As such it is a collection of methods for supervised learning.
Pattern recognition aims to classify data (patterns) based on either a priori knowledge or on statistical information extracted from the patterns. The patterns to be classified are usually groups of measurements or observations, defining points in an appropriate multidimensional space.
PATTEREN RECOGNITION AND AI:
The Centre prides itself on its international reputation and leadership role in the area of pattern recognition, the key technology of multimedia signal interpretation. Its research has contributed many of the state of the art methods in various aspects of pattern recognition system design. CVSSP techniques which help to identify the most
Structured Description ( Syntactic Pattern Recognition ):
Syntactic pattern recognition or structural pattern recognition is a form of pattern recognition, in which each object can be represented by a variable-cardinality set of symbolic, nominal features. This allows for representing pattern structures, taking into account more complex interrelationships between attributes than is possible in the case of flat, numerical feature vectors of fixed dimensionality, that are used in statistical classification
Statistical Classification (Decision Theoritic Classification):
Decision-theoretic pattern recognition methods are applied to classifying Ricker wavelets and to detecting waveform anomalies in seismograms. The methods include Bayes decision rule and linear and quadratic classifications. Envelope and instantaneous frequency are extracted as the two features of a seismic trace used as input into the classification schemes. A modified fixed-increment training procedure is employed to solve the decision boundary. The classification schemes successfully distinguish zero-phase Ricker wavelets of different peak frequencies from each other and from random noise.