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A.R. Rao, R.C. Jain, "Computerized Flow Field Analysis: Oriented Texture Fields," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 7, pp. 693709, July, 1992.  
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@article{ 10.1109/34.142908, author = {A.R. Rao and R.C. Jain}, title = {Computerized Flow Field Analysis: Oriented Texture Fields}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {14}, number = {7}, issn = {01628828}, year = {1992}, pages = {693709}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.142908}, 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  Computerized Flow Field Analysis: Oriented Texture Fields IS  7 SN  01628828 SP693 EP709 EPD  693709 A1  A.R. Rao, A1  R.C. Jain, PY  1992 KW  computer vision; computerised pattern recognition; flow field analysis; oriented texture fields; signaltosymbol transformation; velocity vector fields; geometric theory; differential equations; symbol set; phase portraits; symbolic representation; symbolic descriptors; flow visualization pictures; semiconductor wafer inspection; optical flow fields; computerised pattern recognition; computerised picture processing; differential equations; optical information processing VL  14 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
An approach to the solution of signaltosymbol transformation in the domain of flow fields, such as oriented texture fields and velocity vector fields, is discussed. The authors use the geometric theory of differential equations to derive a symbol set based on the visual appearance of phase portraits which are a geometric representation of the solution curves of a system of differential equations. They also provide the computational framework to start with a given flow field and derive its symbolic representation. Specifically, they segment the given texture, derive its symbolic representation, and perform a quantitative reconstruction of the salient features of the original texture based on the symbolic descriptors. Results of applying this technique to several real texture images are presented. This technique is useful in describing complex flow visualization pictures, defects in lumber processing, defects in semiconductor wafer inspection, and optical flow fields.
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