02-04-2011, 11:50 AM
SUBMITTED BY:
C. VIGNESHWARAN.,
S. RANGARAJAN,
[attachment=11547]
ABSTRACT:
INTRODUCTION:
The proposal of our research line is the search for alternatives to the resolution of complex problems where human knowledge should be apprehended in a general fashion.
The solution to the problems can be found in the area of Pattern Recognition, where the solution rests on the easiness with which the systems adapts to the information available, in this case coming from the object. In this sense, neural networks are extremely useful, since they are not only capable of learning with the aid of an expert, but they can also make generalizations based on the information from the input data, thus showing relations that are a priori of a complex nature.
Since we know that,
It’s easy to train a neural network with samples which contain patterns, but it is much harder to train a neural network with samples which do not.
The number of “non-pattern” samples is just too large.
An Example;
Pattern Recognition:
Within the pattern recognition area, one of the most important concepts is that of
discriminant. The existence of discriminants constitutes the essence of pattern recognition.
The main characteristic of neural networks is their ability to generalize information, as well as their tolerance to noise. Therefore, one of the computer science areas that uses them the most is Pattern Recognition
Applications:
The system has been designed to be generally applicable to a variety of real time
applications as follows;
used as a black-and-white mug shot identification system;
with PC-attached cameras for computer logon from a smart-card stored
database;
audio-visual speech recognition (visual lip reading
to enhance acoustic speech recognition)
audio-visual speech recognition (visual lip reading to enhance acoustic speech recognition)
Face Recognition Systems(Face matching, Face detections, Access controls, etc.), etc.
Conclusion
Pattern recognition is a technology just reaching sufficient maturity for it to
experience a rapid growth in its practical applications. Much research effort
around the world is being applied to expanding the accuracy and capabilities
of this biometric domain, with a consequent broadening of its application in
the near future. Verification systems for physical and electronic access security
are available today, but the future holds the promise and the threat of passive
customization and automated surveillance systems enabled by pattern recognition.
C. VIGNESHWARAN.,
S. RANGARAJAN,
[attachment=11547]
ABSTRACT:
INTRODUCTION:
The proposal of our research line is the search for alternatives to the resolution of complex problems where human knowledge should be apprehended in a general fashion.
The solution to the problems can be found in the area of Pattern Recognition, where the solution rests on the easiness with which the systems adapts to the information available, in this case coming from the object. In this sense, neural networks are extremely useful, since they are not only capable of learning with the aid of an expert, but they can also make generalizations based on the information from the input data, thus showing relations that are a priori of a complex nature.
Since we know that,
It’s easy to train a neural network with samples which contain patterns, but it is much harder to train a neural network with samples which do not.
The number of “non-pattern” samples is just too large.
An Example;
Pattern Recognition:
Within the pattern recognition area, one of the most important concepts is that of
discriminant. The existence of discriminants constitutes the essence of pattern recognition.
The main characteristic of neural networks is their ability to generalize information, as well as their tolerance to noise. Therefore, one of the computer science areas that uses them the most is Pattern Recognition
Applications:
The system has been designed to be generally applicable to a variety of real time
applications as follows;
used as a black-and-white mug shot identification system;
with PC-attached cameras for computer logon from a smart-card stored
database;
audio-visual speech recognition (visual lip reading
to enhance acoustic speech recognition)
audio-visual speech recognition (visual lip reading to enhance acoustic speech recognition)
Face Recognition Systems(Face matching, Face detections, Access controls, etc.), etc.
Conclusion
Pattern recognition is a technology just reaching sufficient maturity for it to
experience a rapid growth in its practical applications. Much research effort
around the world is being applied to expanding the accuracy and capabilities
of this biometric domain, with a consequent broadening of its application in
the near future. Verification systems for physical and electronic access security
are available today, but the future holds the promise and the threat of passive
customization and automated surveillance systems enabled by pattern recognition.
