Titlebook: Knowledge Coupling; New Premises and New Lawrence L. Weed Book 1991 Springer-Verlag New York, Inc. 1991 Medizinische Informatik.care.comput

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ed, and refinement and evaluation methods for box splines are presented in detail. Shape preserving properties, the construction of non-rectangular box spline surfaces, applications to surface modeling, and imbedding problems, are discussed. - Advanced computer graphics techniques for volume visuali

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Lawrence L. Weedhe interaction between the two ?elds. The present volume presents revised papers which have grown out of the 2005 Oslo workshop, which was aligned with the ?nal review of the European project GAIA II, entitled Intersection algorithms for geometry based IT-applications 4 using approximate algebraic m

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Lawrence L. Weedhe interaction between the two ?elds. The present volume presents revised papers which have grown out of the 2005 Oslo workshop, which was aligned with the ?nal review of the European project GAIA II, entitled Intersection algorithms for geometry based IT-applications 4 using approximate algebraic m

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Lawrence L. Weeduction, freeform product modeling, constraining and reconstructing freeform products, and shape optimization for improving the manufacturability of freeform products. Based on these techniques, the design automation problem for human-centered freeform products can be fundamentally solved. . .Researc

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e structurally meaningful feature lines instead of using just the points that are structurally meaningless. Randomization is used to boost its performance in hard cases. Experimental results show that RICC achieves fast convergence in all cases, regardless of input surfaces’ pose.

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Lawrence L. WeedThe algorithm performs work proportionate to the tiny compressed output size, rather than to some arbitrarily high resolution that would otherwise be required, thus increasing performance by several orders of magnitude..We describe several design operations that are made tractable because of the pro

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e structurally meaningful feature lines instead of using just the points that are structurally meaningless. Randomization is used to boost its performance in hard cases. Experimental results show that RICC achieves fast convergence in all cases, regardless of input surfaces’ pose.

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