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\title{NeuroRoughtSetsLab an Intelligent Data Processing
Software Laboratory for Rough Sets, Neural Networks and
Image Processing}
\author{Roman W. Swiniarski, Jinwoo Lee \\
Department of Mathematical Sciences Computer Science\\
San Diego State University, San Diego, CA 92182-0314 U.S.A.}
\date{}
\begin{document}
\maketitle
\section{Introduction}
In numerous studies, neural networks of various architectures have been
shown to be effective in learning nonlinear, multi-dimensional input-output
maps suitable for high performance computation as well as for classification 
of vague and noisy patterns.\\

While neural networks have been shown to be effective in learning multi-input,
multi-output mapping and classification, the problem remains of
preprocessing raw pictorial patterns before being applied as inputs to 
neural network.  Among other important problem in pattern recognition these
of particular importance can be listed as follows:

\begin{itemize}
\item{Preprocessing and transformation of raw patterns}
\item{Extraction of relevant features from the patterns.}
\item{Processing and discretizing of continuous value features and overlapping
patterns}
\item{Selecting an optimal, reduced set of invariant features of raw
input patterns ensuring the best mapping or classification for given domain
of patterns and providing the best generalization.}

\item{Evaluation sensitiveness (importance) of extracted pattern features for
the given mapping or classification problem.}

\item{Resolving the problem of ``overlapping patterns''}
\item{Preparing optimal training and testing sets for the best generalization.}
\item{Designing of generalizing and adaptive classifiers.}
\item{Computing a quality of selected features patterns from the point of view
quality and generalization of classification.}
\end{itemize}

\vskip 2truemm

Rough sets introduced by Z. Pawlak (Pawlak, 1992) has been found applicable 
for an intelligent data processing and discovering of hidden relations in
informations.  Particular interesting impact rough sets contributed to the
area of classification. \\

The major advantage of the rough sets is ability in the strict mathematical
terms to reduce the set of attributes while maintaining the satisfactory
classification accuracy.  In the neural network technique of classification
the feature set reduction, which relates also to the training set reduction,
is considered to be fundamentally important from the point of view of 
network generalization.\\

\section{Objective}
The paper describes the NeuroRoughSetsLab for analysis of imagery patterns
and designing of generalizing and adaptive classifiers using of rough set 
and neural networks techniques.  The NeuroRoughSetsLab provides:

\begin{itemize}
\item{Preprocessing and editing of raw images form the organized database
of patterns.}
\item{Automatic extraction of invariant features from imageries.}
\item{Discretizing of continuous value features.}
\item{Selecting the best reduced set of extracted features of imagery 
patterns for neural network based classifier.}
\item{Designing of rough sets and neural networks based classifiers.}
\end{itemize}

The complex Zernike and Legendre moments (Swiniarski, 1993) has been chosen
as an automatic invariant feature extraction technique from character 
imageries. \\

New technique for dividing of continuous feature spaces into region suitable
for an application of rough sets for reduction of feature sets.  The
statistical clustering as well as probability density estimation has been
implemented. \\

\begin{thebibliography}{100}
\bibitem{b1}{Pawlak, Z. (1992).  Rough Sets.  Kluwer, NY. 1992.}
\bibitem{b2}{Skowron, A. (1990). The rough sets theory and evidence theory.
Fundamenta Informaticae vol.13 pp.245-262.}
\bibitem{b3}{R. Swiniarski. ``Introduction to Neural Networks''.  In ``
Handbook of Cybernetics and Systems''. C. V. Negoita, ed., Marcel Dekker,
inc. New York 1991.}
\bibitem{b4}{R. Swiniarski. ``Introduction to Rough Sets''.  Materials of
The International Short Course ``Neural Networks.  Fuzzy and Rough Systems.''
Theory and Applications.'' April 2, 1993.  San Diego State University.}
\bibitem{b5}{R. Swiniarski. ``High Order Zernike Moments for the Shift,
Scale and Rotation Invariant Pattern Recognition''. Internal paper of SDSU,
1993.}
\bibitem{b6}{R. Swiniarski. ``NetGen - Interactive Platform for Neural 
Network Simulation''. Proceedings of World Congress on Neural Network 1994
(WCNN '94).  June 4-9, 1994, San Diego California, U.S.A. (coauthors: 
S.Logan, and N. Marovac).}
\end{thebibliography}

\end{document}







