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R.J.T. Morris, L.D. Rubin, H. Tirri, "Neural Network Techniques for Object Orientation Detection: Solution by Optimal Feedforward Network and Learning Vector Quantization Approaches," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 11, pp. 11071115, November, 1990.  
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@article{ 10.1109/34.61712, author = {R.J.T. Morris and L.D. Rubin and H. Tirri}, title = {Neural Network Techniques for Object Orientation Detection: Solution by Optimal Feedforward Network and Learning Vector Quantization Approaches}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {12}, number = {11}, issn = {01628828}, year = {1990}, pages = {11071115}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.61712}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Neural Network Techniques for Object Orientation Detection: Solution by Optimal Feedforward Network and Learning Vector Quantization Approaches IS  11 SN  01628828 SP1107 EP1115 EPD  11071115 A1  R.J.T. Morris, A1  L.D. Rubin, A1  H. Tirri, PY  1990 KW  symbol orientation; mark orientation; pattern recognition; character recognition; object orientation detection; optimal feedforward network; learning vector quantization; computervision; automatic inspection; highdimensional classification problem; optimal Bayesian detector; parallel structure; learning vector quantization neural network; automatic optical inspection; computer vision; computerised pattern recognition; learning systems; neural nets VL  12 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
The computervision problem of determining object orientation from the consensus of orientations of individual symbols or marks is examined. The problem arises in automatic inspection where orientation can be detected from printed text but there is no knowledge of the content of the text. This is a highdimensional classification problem, and there is a requirement for highly accurate detection and rapid processing. The typical multilayer threshold networks are seen as unsuitable, and the optimal Bayesian detector is derived and found to have the highly parallel structure of a feedforward network. The learning vector quantization neural network method of T. Kohonen (1988) is also applied. Experimental results, comparisons, and a complete implementation are described.
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