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data/tanveer/statlog-vehicle/vehicle.doc
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data/tanveer/statlog-vehicle/vehicle.doc
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!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!IMPORTANT!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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This dataset comes from the Turing Institute, Glasgow, Scotland.
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If you use this dataset in any publication you must acknowledge this
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source.
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!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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NAME
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vehicle silhouettes
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PURPOSE
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to classify a given silhouette as one of four types of vehicle,
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using a set of features extracted from the silhouette. The
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vehicle may be viewed from one of many different angles.
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PROBLEM TYPE
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classification
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SOURCE
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Drs.Pete Mowforth and Barry Shepherd
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Turing Institute
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George House
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36 North Hanover St.
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Glasgow
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G1 2AD
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CONTACT
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Alistair Sutherland
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Statistics Dept.
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Strathclyde University
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Livingstone Tower
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26 Richmond St.
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GLASGOW G1 1XH
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Great Britain
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Tel: 041 552 4400 x3033
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Fax: 041 552 4711
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e-mail: alistair@uk.ac.strathclyde.stams
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HISTORY
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This data was originally gathered at the TI in 1986-87 by
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JP Siebert. It was partially financed by Barr and Stroud Ltd.
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The original purpose was to find a method of distinguishing
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3D objects within a 2D image by application of an ensemble of
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shape feature extractors to the 2D silhouettes of the objects.
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Measures of shape features extracted from example silhouettes
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of objects to be discriminated were used to generate a class-
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ification rule tree by means of computer induction.
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This object recognition strategy was successfully used to
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discriminate between silhouettes of model cars, vans and buses
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viewed from constrained elevation but all angles of rotation.
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The rule tree classification performance compared favourably
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to MDC (Minimum Distance Classifier) and k-NN (k-Nearest Neigh-
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bour) statistical classifiers in terms of both error rate and
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computational efficiency. An investigation of these rule trees
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generated by example indicated that the tree structure was
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heavily influenced by the orientation of the objects, and grouped
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similar object views into single decisions.
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DESCRIPTION
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The features were extracted from the silhouettes by the HIPS
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(Hierarchical Image Processing System) extension BINATTS, which
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extracts a combination of scale independent features utilising
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both classical moments based measures such as scaled variance,
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skewness and kurtosis about the major/minor axes and heuristic
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measures such as hollows, circularity, rectangularity and
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compactness.
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Four "Corgie" model vehicles were used for the experiment:
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a double decker bus, Cheverolet van, Saab 9000 and an Opel Manta 400.
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This particular combination of vehicles was chosen with the
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expectation that the bus, van and either one of the cars would
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be readily distinguishable, but it would be more difficult to
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distinguish between the cars.
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The images were acquired by a camera looking downwards at the
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model vehicle from a fixed angle of elevation (34.2 degrees
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to the horizontal). The vehicles were placed on a diffuse
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backlit surface (lightbox). The vehicles were painted matte black
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to minimise highlights. The images were captured using a CRS4000
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framestore connected to a vax 750. All images were captured with
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a spatial resolution of 128x128 pixels quantised to 64 greylevels.
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These images were thresholded to produce binary vehicle silhouettes,
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negated (to comply with the processing requirements of BINATTS) and
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thereafter subjected to shrink-expand-expand-shrink HIPS modules to
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remove "salt and pepper" image noise.
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The vehicles were rotated and their angle of orientation was measured
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using a radial graticule beneath the vehicle. 0 and 180 degrees
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corresponded to "head on" and "rear" views respectively while 90 and
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270 corresponded to profiles in opposite directions. Two sets of
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60 images, each set covering a full 360 degree rotation, were captured
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for each vehicle. The vehicle was rotated by a fixed angle between
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images. These datasets are known as e2 and e3 respectively.
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A further two sets of images, e4 and e5, were captured with the camera
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at elevations of 37.5 degs and 30.8 degs respectively. These sets
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also contain 60 images per vehicle apart from e4.van which contains
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only 46 owing to the difficulty of containing the van in the image
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at some orientations.
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ATTRIBUTES
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COMPACTNESS (average perim)**2/area
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CIRCULARITY (average radius)**2/area
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DISTANCE CIRCULARITY area/(av.distance from border)**2
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RADIUS RATIO (max.rad-min.rad)/av.radius
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PR.AXIS ASPECT RATIO (minor axis)/(major axis)
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MAX.LENGTH ASPECT RATIO (length perp. max length)/(max length)
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SCATTER RATIO (inertia about minor axis)/(inertia about major axis)
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ELONGATEDNESS area/(shrink width)**2
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PR.AXIS RECTANGULARITY area/(pr.axis length*pr.axis width)
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MAX.LENGTH RECTANGULARITY area/(max.length*length perp. to this)
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SCALED VARIANCE (2nd order moment about minor axis)/area
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ALONG MAJOR AXIS
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SCALED VARIANCE (2nd order moment about major axis)/area
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ALONG MINOR AXIS
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SCALED RADIUS OF GYRATION (mavar+mivar)/area
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SKEWNESS ABOUT (3rd order moment about major axis)/sigma_min**3
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MAJOR AXIS
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SKEWNESS ABOUT (3rd order moment about minor axis)/sigma_maj**3
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MINOR AXIS
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KURTOSIS ABOUT (4th order moment about major axis)/sigma_min**4
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MINOR AXIS
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KURTOSIS ABOUT (4th order moment about minor axis)/sigma_maj**4
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MAJOR AXIS
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HOLLOWS RATIO (area of hollows)/(area of bounding polygon)
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Where sigma_maj**2 is the variance along the major axis and
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sigma_min**2 is the variance along the minor axis, and
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area of hollows= area of bounding poly-area of object
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The area of the bounding polygon is found as a side result of
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the computation to find the maximum length. Each individual
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length computation yields a pair of calipers to the object
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orientated at every 5 degrees. The object is propagated into
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an image containing the union of these calipers to obtain an
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image of the bounding polygon.
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NUMBER OF CLASSES
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4 OPEL, SAAB, BUS, VAN
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NUMBER OF EXAMPLES
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Total no. = 946
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No. in each class
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opel 240
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saab 240
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bus 240
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van 226
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100 examples are being kept by Strathclyde for validation.
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So StatLog partners will receive 846 examples.
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NUMBER OF ATTRIBUTES
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No. of atts. = 18
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BIBLIOGRAPHY
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Turing Institute Research Memorandum TIRM-87-018 "Vehicle
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Recognition Using Rule Based Methods" by Siebert,JP (March 1987)
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