05-05-2011, 02:54 PM
Abstract
Among the various traditional approaches of pattern recognition the statistical approach
has been most intensively studied and used in practice. More recently, the addition of
artificial neural network techniques theory have been receiving significant attention. The
design of a recognition system requires careful attention to the following issues: definition of
pattern classes, sensing environment, pattern representation, feature extraction and selection,
cluster analysis, classifier design and learning, selection of training and test samples, and
performance evaluation. In spite of almost 50 years of research and development in this field,
the general problem of recognizing complex patterns with arbitrary orientation, location, and
scale remains unsolved. New and emerging applications, such as data mining, web searching,
retrieval of multimedia data, face recognition, and cursive handwriting recognition, require
robust and efficient pattern recognition techniques. The objective of this review paper is to
summarize and compare some of the well-known methods used in various stages of a pattern
recognition system using ANN and identify research topics and applications which are at the
forefront of this exciting and challenging field.
Keywords: Pattern Recognition, correlation, Neural Network.
1. Introduction
Pattern recognition is the study of how machines can observe the environment, learn to
distinguish patterns of interest from their background, and make sound and reasonable
decisions about the categories of the patterns. In spite of almost 50 years of research, design
of a general purpose machine pattern recognizer remains an elusive goal. The best pattern
recognizers in most instances are humans, yet we do not understand how humans recognize
patterns. Ross [1] emphasizes the work of Nobel Laureate Herbert Simon whose central
finding was that pattern recognition is critical in most human decision making tasks: “The
more relevant patterns at your disposal, the better your decisions will be. This is hopeful news
to proponents of artificial intelligence, since computers can surely be taught to recognize
patterns. Indeed, successful computer programs that help banks score credit applicants, help
doctors diagnose disease and help pilots land airplanes depend in some way on pattern
recognition... We need to pay much more explicit attention to teaching pattern recognition”.
*Corresponding Author
International Journal of Software Engineering and Its Applications
Vol. 4, No. 2, April 2010
24
Our goal here is to introduce pattern recognition using artificial neural network as the best
possible way of utilizing available sensors, processors, and domain knowledge to make
decisions automatically.
2. Pattern Recognition
Automatic (machine) recognition, description, classification, and grouping of
patterns are important problems in a variety of engineering and scientific disciplines
such as biology, psychology, medicine, marketing, computer vision, artificial
intelligence, and remote sensing. A pattern could be a fingerprint image, a handwritten
cursive word, a human face, or a speech signal. Given a pattern, its
recognition/classification may consist of one of the following two tasks: 1) supervised
classification (e.g., discriminant analysis) in which the input pattern is identified as a
member of a predefined class, 2) unsupervised classification (e.g., clustering) in which
the pattern is assigned to a hitherto unknown class. The recognition problem here is
being posed as a classification or categorization task, where the classes are either
defined by the system designer (in supervised classification) or are learned based on the
similarity of patterns (in unsupervised classification).These applications include data
mining (identifying a “pattern”, e.g., correlation, or an outlier in millions of
multidimensional patterns), document classification (efficiently searching text
documents), financial forecasting, organization and retrieval of multimedia databases,
and biometrics. The rapidly growing and available computing power, while enabling
faster processing of huge data sets, has also facilitated the use of elaborate and diverse
methods for data analysis and classification. At the same time, demands on automatic
pattern recognition systems are rising enormously due to the availability of large
databases and stringent performance requirements (speed, accuracy, and cost). The
design of a pattern recognition system essentially involves the following three aspects:
1) data acquisition and preprocessing, 2) data representation, and 3) decision making.
The problem domain dictates the choice of sensor(s), preprocessing technique,
representation scheme, and the decision making model. It is generally agreed that a
well-defined and sufficiently constrained recognition problem (small intraclass
variations and large interclass variations) will lead to a compact pattern representation
and a simple decision making strategy. Learning from a set of examples (training set) is
an important and desired attribute of most pattern recognition systems. The four best
known approaches for pattern recognition are: 1) template matching, 2) statistical
classification, 3) syntactic or structural matching, and 4) neural networks.
Download full report
http://www.serscjournals/IJSEIA/vol4_no2_2010/3.pdf
Among the various traditional approaches of pattern recognition the statistical approach
has been most intensively studied and used in practice. More recently, the addition of
artificial neural network techniques theory have been receiving significant attention. The
design of a recognition system requires careful attention to the following issues: definition of
pattern classes, sensing environment, pattern representation, feature extraction and selection,
cluster analysis, classifier design and learning, selection of training and test samples, and
performance evaluation. In spite of almost 50 years of research and development in this field,
the general problem of recognizing complex patterns with arbitrary orientation, location, and
scale remains unsolved. New and emerging applications, such as data mining, web searching,
retrieval of multimedia data, face recognition, and cursive handwriting recognition, require
robust and efficient pattern recognition techniques. The objective of this review paper is to
summarize and compare some of the well-known methods used in various stages of a pattern
recognition system using ANN and identify research topics and applications which are at the
forefront of this exciting and challenging field.
Keywords: Pattern Recognition, correlation, Neural Network.
1. Introduction
Pattern recognition is the study of how machines can observe the environment, learn to
distinguish patterns of interest from their background, and make sound and reasonable
decisions about the categories of the patterns. In spite of almost 50 years of research, design
of a general purpose machine pattern recognizer remains an elusive goal. The best pattern
recognizers in most instances are humans, yet we do not understand how humans recognize
patterns. Ross [1] emphasizes the work of Nobel Laureate Herbert Simon whose central
finding was that pattern recognition is critical in most human decision making tasks: “The
more relevant patterns at your disposal, the better your decisions will be. This is hopeful news
to proponents of artificial intelligence, since computers can surely be taught to recognize
patterns. Indeed, successful computer programs that help banks score credit applicants, help
doctors diagnose disease and help pilots land airplanes depend in some way on pattern
recognition... We need to pay much more explicit attention to teaching pattern recognition”.
*Corresponding Author
International Journal of Software Engineering and Its Applications
Vol. 4, No. 2, April 2010
24
Our goal here is to introduce pattern recognition using artificial neural network as the best
possible way of utilizing available sensors, processors, and domain knowledge to make
decisions automatically.
2. Pattern Recognition
Automatic (machine) recognition, description, classification, and grouping of
patterns are important problems in a variety of engineering and scientific disciplines
such as biology, psychology, medicine, marketing, computer vision, artificial
intelligence, and remote sensing. A pattern could be a fingerprint image, a handwritten
cursive word, a human face, or a speech signal. Given a pattern, its
recognition/classification may consist of one of the following two tasks: 1) supervised
classification (e.g., discriminant analysis) in which the input pattern is identified as a
member of a predefined class, 2) unsupervised classification (e.g., clustering) in which
the pattern is assigned to a hitherto unknown class. The recognition problem here is
being posed as a classification or categorization task, where the classes are either
defined by the system designer (in supervised classification) or are learned based on the
similarity of patterns (in unsupervised classification).These applications include data
mining (identifying a “pattern”, e.g., correlation, or an outlier in millions of
multidimensional patterns), document classification (efficiently searching text
documents), financial forecasting, organization and retrieval of multimedia databases,
and biometrics. The rapidly growing and available computing power, while enabling
faster processing of huge data sets, has also facilitated the use of elaborate and diverse
methods for data analysis and classification. At the same time, demands on automatic
pattern recognition systems are rising enormously due to the availability of large
databases and stringent performance requirements (speed, accuracy, and cost). The
design of a pattern recognition system essentially involves the following three aspects:
1) data acquisition and preprocessing, 2) data representation, and 3) decision making.
The problem domain dictates the choice of sensor(s), preprocessing technique,
representation scheme, and the decision making model. It is generally agreed that a
well-defined and sufficiently constrained recognition problem (small intraclass
variations and large interclass variations) will lead to a compact pattern representation
and a simple decision making strategy. Learning from a set of examples (training set) is
an important and desired attribute of most pattern recognition systems. The four best
known approaches for pattern recognition are: 1) template matching, 2) statistical
classification, 3) syntactic or structural matching, and 4) neural networks.
Download full report
http://www.serscjournals/IJSEIA/vol4_no2_2010/3.pdf