\HeaderA{Vehicle}{Vehicle Silhouettes}{Vehicle}
\keyword{datasets}{Vehicle}
\begin{Description}\relax
The purpose is to classify a given silhouette as one of four types
of vehicle, using a set of features extracted from the
silhouette. The vehicle may be viewed from one of many different
angles. The features were extracted from the silhouettes by the HIPS
(Hierarchical Image Processing System) extension BINATTS, which
extracts a combination of scale independent features utilising both
classical moments based measures such as scaled variance, skewness
and kurtosis about the major/minor axes and heuristic measures such
as hollows, circularity, rectangularity and compactness. 

Four "Corgie" model vehicles were used for the experiment: a double
decker bus, Cheverolet van, Saab 9000 and an Opel Manta 400. This
particular combination of vehicles was chosen with the expectation
that the bus, van and either one of the cars would be readily
distinguishable, but it would be more difficult to distinguish
between the cars.
\end{Description}
\begin{Usage}
\begin{verbatim}data(Vehicle)\end{verbatim}
\end{Usage}
\begin{Format}\relax
A data frame with 846 observations on 19 variables, all numerical
and one nominal defining the class of the objects.

\Tabular{cll}{
[,1] & Comp & Compactness\\{}
[,2] & Circ & Circularity\\{}
[,3] & D.Circ & Distance Circularity\\{}
[,4] & Rad.Ra & Radius ratio\\{}
[,5] & Pr.Axis.Ra & pr.axis aspect ratio\\{}
[,6] & Max.L.Ra & max.length aspect ratio\\{}
[,7] & Scat.Ra & scatter ratio\\{}
[,8] & Elong & elongatedness\\{}
[,9] & Pr.Axis.Rect & pr.axis rectangularity\\{}
[,10] & Max.L.Rect & max.length rectangularity\\{}
[,11] & Sc.Var.Maxis & scaled variance along major axis\\{}
[,12] & Sc.Var.maxis & scaled variance along minor axis\\{}
[,13] & Ra.Gyr & scaled radius of gyration\\{}
[,14] & Skew.Maxis & skewness about major axis\\{}
[,15] & Skew.maxis & skewness about minor axis\\{}
[,16] & Kurt.maxis & kurtosis about minor axis\\{}
[,17] & Kurt.Maxis & kurtosis about major axis\\{}
[,18] & Holl.Ra & hollows ratio\\{}
[,19] & Class & type
}
\end{Format}
\begin{Source}\relax
\Itemize{
\item Creator: Drs.Pete Mowforth and Barry Shepherd, Turing
Institute, Glasgow, Scotland.   
}

These data have been taken from the UCI Repository Of Machine Learning
Databases at
\Itemize{
\item \url{ftp://ftp.ics.uci.edu/pub/machine-learning-databases}
\item \url{http://www.ics.uci.edu/~mlearn/MLRepository.html}
}
and were converted to R format by Evgenia.Dimitriadou@ci.tuwien.ac.at.
\end{Source}
\begin{References}\relax
Turing Institute Research Memorandum TIRM-87-018 "Vehicle
Recognition Using Rule Based Methods" by Siebert,JP (March 1987)
\end{References}

