% !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!IMPORTANT!!!!!!!!!!!!!!!!!!!!!!!!!!!!% %         This dataset comes from the Turing Institute, Glasgow, Scotland.%         If you use this dataset in any publication you must acknowledge this%         source.% % !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!% % 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)% % @relation vehicle@attribute 'COMPACTNESS' real@attribute 'CIRCULARITY' real@attribute 'DISTANCE CIRCULARITY' real@attribute 'RADIUS RATIO' real@attribute 'PR.AXIS ASPECT RATIO' real@attribute 'MAX.LENGTH ASPECT RATIO' real@attribute 'SCATTER RATIO' real@attribute 'ELONGATEDNESS' real@attribute 'PR.AXIS RECTANGULARITY' real@attribute 'MAX.LENGTH RECTANGULARITY' real@attribute 'SCALED VARIANCE_MAJOR' real@attribute 'SCALED VARIANCE_MINOR' real@attribute 'SCALED RADIUS OF GYRATION'  real@attribute 'SKEWNESS ABOUT_MAJOR' real@attribute 'SKEWNESS ABOUT_MINOR' real@attribute 'KURTOSIS ABOUT_MAJOR' real@attribute 'KURTOSIS ABOUT_MINOR' real@attribute 'HOLLOWS RATIO' real@attribute 'Class' {opel,saab,bus,van}@data95,48,83,178,72,10,162,42,20,159,176,379,184,70,6,16,187,197,van91,41,84,141,57,9,149,45,19,143,170,330,158,72,9,14,189,199,van104,50,106,209,66,10,207,32,23,158,223,635,220,73,14,9,188,196,saab