Fast Simplex Optimization for Active Appearance Model
Fast Simplex Optimization for Active Appearance Model
复制标题
主动外观模型的快速单纯形优化
DOI：
10.1007/978-3-540-92957-4_10
复制
发表时间：
2009
期刊：
影响因子：
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通讯作者：
R. Séguier
中科院分区：
文献类型：
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作者：
Yasser Aidarous;R. Séguier
针对不同表情下的嘴部对齐问题，提出了一种基于Nelder & Mead单纯形的主动外观模型快速优化方法。这种优化定义了一个新的约束空间。它使用高斯混合来初始化和约束最优解的搜索。将高斯混合应用于表示由主成分分析给出的减少的数据的主特征向量。新的算法约束避免了不代表所研究的形式和纹理的解决方案的计算错误。在每次迭代中，单纯形中加入的约束算子验证解是否属于研究空间。在两个数据集上进行的泛化测试（学习和测试数据集不同）表明，与经典优化的AAM相比，我们的方法具有更好的收敛速度和更少的计算时间。
This paper presents a fast optimization method for active appearance model based on Nelder & Mead simplex in the case of mouth alignment under different expressions. This optimization defines a new constraint space. It uses a Gaussian mixture to initialize and constraint the search of an optimal solution. The Gaussian mixture is applied on the dominant eigenvectors representing the reduced data given by Principal Component Analysis. The new algorithm constraints avoid calculating errors of solutions that don't represent researched forms and textures. The constraint operator added to simplex verifies in each iteration that the solution belongs to the space of research. The tests performed in the context of generalization (learning and testing datasets are different) on two datasets show that our method presents a better convergence rate and less computation time compared to the AAM classically optimized.