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Rony goldenthal thesis


rony goldenthal thesis

: 2002, publication period end: 2002, number of co-authors: 3, co-authors, number of publications with favourite co-authors. The resulting algorithm acts. The motion of the rest of the hairs is derived through interpolation, these hairs. Tation performed with a mutation probability. Optimization variables: Knot vector Parameterization nurbs weights 39 Summary Support for various design objective: Curves: Length Curvature Approximation Error L 2 /L inf Surfaces: Curvature Wilmore surfaces Surface Area Approximation error L 2,L inf 40 Thank You! Bercovier: Optimal Control in cagd: Decouple fitting from design For each knot/parametrization configuration: Solve the state equation (fitting) Evaluate the cost function Minimize the cost function (design objective) 6, standard Approach :Weighted Sum of Objective Functions Limitations: Result depends on weights. In each generation the non-dominated set is maintained, fitness is adjusted according to the domination of each individual. 47 GA - Crossover Modified one point crossover used for each chromosome with the following modification: The sum of all the intervals that make the knot vector and the parametrization must remain fixed. 4, multiple Objective Functions Suppose we want to incorporate several design objectives into a single optimization process: Approximation error under L 2 or L inf Elastic energy Length/Area Class A criterions How to optimize several functions at once?

Design of Curves and Surfaces by Multi Objective Optimization



rony goldenthal thesis

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Ossover performed with a crossover probability. Current facial motion capture methods fail to accurately reproduce motions in those areas due to multiple limitations. 5.Test for end condition, stop, and return the best solution in current population.Loop Go to step. Bercovier April 2004 Computing - Geometric modelling dagstuhl 2002: Volume 72 Issue 1-2, April 2004 Publisher: Springer-Verlag New York, Inc. 43 Genetic Algorithms in CAD Genetic algorithms in CAD,. Place the old population with the new one. 20, multi Objective Genetic Algorithm cont. The new method operates. 46 GA Fitness Evaluation Interpolation/approximation must be performed prior to fitness evaluation. ronygold 41 Extra slides 42 Applications Surface fitting and design has numerous applications mainly in theses areas: Industrial design. 8, genetic algorithms for the single objective problem The control variables: knot vector, parametrization and nurbs weights modeled as chromosomes. One for the nurbs weights.

Cepting Place new offspring in the new population. X 1 is strictly better than x 2 in at least one objective. Keywords : Schoenberg-whitney condition, interpolation, knot vector placement, curve fitting, optimal control 6 Bounded-distortion piecewise mesh parameterization Olga Sorkine, Daniel Cohen-Or, Rony Goldenthal, Dani Lischinski October 2002 VIS '02: Proceedings of the conference on Visualization '02 Publisher: ieee Computer Society Bibliometrics : Citation Count:. Váncza 44 GA - Encoding Each individual contains 3 chromosomes: One for Parametrization. Crossover 15, mutation 16, multi Objective Genetic Algorithm Multi objective optimization has an optimal solution set. 19, pareto Optimal Set Non-dominated set: the set of all solutions which are not dominated by any other solution in the sampled search space. Publisher: ACM, bibliometrics : Citation Count: 16, downloads (6 Weeks  5,  Downloads (12 Months  38,  Downloads (Overall  538, full text available: PDF.


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