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M. Das, M.J. Paulik, N.K. Loh, "A Bivariate Autoregressive Technique for Analysis and Classification of Planar Shapes," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 1, pp. 97103, January, 1990.  
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@article{ 10.1109/34.41389, author = {M. Das and M.J. Paulik and N.K. Loh}, title = {A Bivariate Autoregressive Technique for Analysis and Classification of Planar Shapes}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {12}, number = {1}, issn = {01628828}, year = {1990}, pages = {97103}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.41389}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  A Bivariate Autoregressive Technique for Analysis and Classification of Planar Shapes IS  1 SN  01628828 SP97 EP103 EPD  97103 A1  M. Das, A1  M.J. Paulik, A1  N.K. Loh, PY  1990 KW  pattern recognition; statistical analysis; bivariate autoregressive technique; classification; planar shapes; boundary coordinate sequence; digitized binary image; polygonal approximation; circular sample sequence; vector autoregressive difference equation; estimated coefficient matrices; residual error covariance matrices; difference equations; pattern recognition; statistical analysis VL  12 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
A bivariate autoregressive model is introduced for the analysis and classification of closed planar shapes. The boundary coordinate sequence of a digitized binary image is sampled to produce a polygonal approximation to an object's shape. This circular sample sequence is then represented by a vector autoregressive difference equation which models the individual Cartesian coordinate sequences as well as coordinate interdependencies. Several classification features which are functions or transformations of the estimated coefficient matrices and the associated residual error covariance matrices are developed. These features are shown to be invariant to object transformations such as translation, rotation, and scaling. Laboratory experiments involving object sets representative of industrial shapes are presented. Superior classification results are demonstrated.
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