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D. Terzopoulos, "The Computation of VisibleSurface Representations," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, no. 4, pp. 417438, July, 1988.  
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@article{ 10.1109/34.3908, author = {D. Terzopoulos}, title = {The Computation of VisibleSurface Representations}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {10}, number = {4}, issn = {01628828}, year = {1988}, pages = {417438}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.3908}, 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  The Computation of VisibleSurface Representations IS  4 SN  01628828 SP417 EP438 EPD  417438 A1  D. Terzopoulos, PY  1988 KW  finite element shape primitives; picture processing; pattern recognition; visiblesurface representations; multiscale constraints; surface depth; orientation; interpolation; surface model; discretization; variational principle; finite element analysis; interpolation; pattern recognition; picture processing VL  10 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
A computational theory of visiblesurface representations is developed. The visiblesurface reconstruction process that computes these quantitative representations unifies formal solutions to the key problems of: (1) integrating multiscale constraints on surface depth and orientation from multiplevisual sources; (2) interpolating dense, piecewisesmooth surfaces from these constraints; (3) detecting surface depth and orientation discontinuities to apply boundary conditions on interpolation; and (4) structuring largescale, distributedsurface representations to achieve computational efficiency. Visiblesurface reconstruction is an inverse problem. A wellposed variational formulation results from the use of a controlledcontinuity surface model. Discontinuity detection amounts to the identification of this generic model's distributed parameters from the data. Finiteelement shape primitives yield a local discretization of the variational principle. The result is an efficient algorithm for visiblesurface reconstruction.
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