stree_datasets/data/tanveer/statlog-vehicle/vehicle.doc

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!IMPORTANT!!!!!!!!!!!!!!!!!!!!!!!!!!!!

	This dataset comes from the Turing Institute, Glasgow, Scotland.
	If you use this dataset in any publication you must acknowledge this
	source.

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NAME
	vehicle silhouettes

PURPOSE
	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.  

PROBLEM TYPE
	classification
	
SOURCE
	Drs.Pete Mowforth and Barry Shepherd
	Turing Institute
	George House
	36 North Hanover St.
	Glasgow
	G1 2AD

CONTACT
	Alistair Sutherland
	Statistics Dept.
	Strathclyde University
	Livingstone Tower
	26 Richmond St.
	GLASGOW G1 1XH
	Great Britain
	
	Tel: 041 552 4400 x3033
	
	Fax: 041 552 4711 
	
	e-mail: alistair@uk.ac.strathclyde.stams

HISTORY
	This data was originally gathered at the TI in 1986-87 by
	JP Siebert. It was partially financed by Barr and Stroud Ltd.
	The original purpose was to find a method of distinguishing
	3D objects within a 2D image by application of an ensemble of
	shape feature extractors to the 2D silhouettes of the objects.
	Measures of shape features extracted from example silhouettes
	of objects to be discriminated were used to generate a class-
	ification rule tree by means of computer induction.
	 This object recognition strategy was successfully used to 
	discriminate between silhouettes of model cars, vans and buses
	viewed from constrained elevation but all angles of rotation.
	 The rule tree classification performance compared favourably
	to MDC (Minimum Distance Classifier) and k-NN (k-Nearest Neigh-
	bour) statistical classifiers in terms of both error rate and
	computational efficiency. An investigation of these rule trees
	generated by example indicated that the tree structure was 
	heavily influenced by the orientation of the objects, and grouped
	similar object views into single decisions.

DESCRIPTION
	 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.
	 The images were acquired by a camera looking downwards at the
	model vehicle from a fixed angle of elevation (34.2 degrees
	to the horizontal). The vehicles were placed on a diffuse
	backlit surface (lightbox). The vehicles were painted matte black
	to minimise highlights. The images were captured using a CRS4000
	framestore connected to a vax 750. All images were captured with
	a spatial resolution of 128x128 pixels quantised to 64 greylevels.
	These images were thresholded to produce binary vehicle silhouettes,
	negated (to comply with the processing requirements of BINATTS) and
	thereafter subjected to shrink-expand-expand-shrink HIPS modules to
	remove "salt and pepper" image noise.
	 The vehicles were rotated and their angle of orientation was measured
	using a radial graticule beneath the vehicle. 0 and 180 degrees
	corresponded to "head on" and "rear" views respectively while 90 and
	270 corresponded to profiles in opposite directions. Two sets of
	60 images, each set covering a full 360 degree rotation, were captured
	for each vehicle. The vehicle was rotated by a fixed angle between 
	images. These datasets are known as e2 and e3 respectively.
	 A further two sets of images, e4 and e5, were captured with the camera 
	at elevations of 37.5 degs and 30.8 degs respectively. These sets
	also contain 60 images per vehicle apart from e4.van which contains
	only 46 owing to the difficulty of containing the van in the image
	at some orientations.

ATTRIBUTES
	
	COMPACTNESS	(average perim)**2/area
	
	CIRCULARITY	(average radius)**2/area
	
	DISTANCE CIRCULARITY	area/(av.distance from border)**2
	
	RADIUS RATIO	(max.rad-min.rad)/av.radius
	
	PR.AXIS ASPECT RATIO	(minor axis)/(major axis)
	
	MAX.LENGTH ASPECT RATIO	(length perp. max length)/(max length)
	
	SCATTER RATIO	(inertia about minor axis)/(inertia about major axis)
	
	ELONGATEDNESS		area/(shrink width)**2
	
	PR.AXIS RECTANGULARITY	area/(pr.axis length*pr.axis width)
	
	MAX.LENGTH RECTANGULARITY area/(max.length*length perp. to this)
	
	SCALED VARIANCE 	(2nd order moment about minor axis)/area
	ALONG MAJOR AXIS
	
	SCALED VARIANCE 	(2nd order moment about major axis)/area
	ALONG MINOR AXIS 
	
	SCALED RADIUS OF GYRATION	(mavar+mivar)/area
	
	SKEWNESS ABOUT 	(3rd order moment about major axis)/sigma_min**3
	MAJOR AXIS
	
	SKEWNESS ABOUT 	(3rd order moment about minor axis)/sigma_maj**3
	MINOR AXIS
	  	
	KURTOSIS ABOUT 	(4th order moment about major axis)/sigma_min**4
	MINOR AXIS  
	  	
	KURTOSIS ABOUT 	(4th order moment about minor axis)/sigma_maj**4
	MAJOR AXIS
	
	HOLLOWS RATIO	(area of hollows)/(area of bounding polygon)
	
	 Where sigma_maj**2 is the variance along the major axis and
	sigma_min**2 is the variance along the minor axis, and
	
	area of hollows= area of bounding poly-area of object 
	
	 The area of the bounding polygon is found as a side result of
	the computation to find the maximum length. Each individual
	length computation yields a pair of calipers to the object
	orientated at every 5 degrees. The object is propagated into
	an image containing the union of these calipers to obtain an
	image of the bounding polygon. 
	
NUMBER OF CLASSES

	4	OPEL, SAAB, BUS, VAN

NUMBER OF EXAMPLES

		Total no. = 946
		
		No. in each class
		
		  opel 240
		  saab 240
		  bus  240
		  van  226
		
		
		100 examples are being kept by Strathclyde for validation.
		So StatLog partners will receive 846 examples.

NUMBER OF ATTRIBUTES

		No. of atts. = 18

	
BIBLIOGRAPHY

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