Vehicle

0th

Percentile

Vehicle Silhouettes

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.

Keywords
datasets
Usage
data(Vehicle)
Format

A data frame with 846 observations on 19 variables, all numerical and one nominal defining the class of the objects.

[,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

Source

  • 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 and were converted to R format by Evgenia Dimitriadou.

References

Turing Institute Research Memorandum TIRM-87-018 "Vehicle Recognition Using Rule Based Methods" by Siebert,JP (March 1987)

Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998). UCI Repository of machine learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science.

Aliases
  • Vehicle
Examples
library(mlbench) data(Vehicle) summary(Vehicle)
Documentation reproduced from package mlbench, version 2.1-1, License: GPL-2

Community examples

Looks like there are no examples yet.