By introducing a smooth merit function for the median function, a new smooth merit function for box constrained variational inequalities (BVIs) was constructed. The function is simple and has some good differential pr...By introducing a smooth merit function for the median function, a new smooth merit function for box constrained variational inequalities (BVIs) was constructed. The function is simple and has some good differential properties. A damped Newton type method was presented based on it.Global and local superlinear/quadratic convergence results were obtained under mild conditions, and the finite termination property was also shown for the linear BVIs. Numerical results suggest that the method is efficient and promising.展开更多
By introducing a smooth merit function for the median function, a new smooth merit function for box constrained variational inequalities （BVIs） was constructed. The function is simple and has some good differential ...By introducing a smooth merit function for the median function, a new smooth merit function for box constrained variational inequalities （BVIs） was constructed. The function is simple and has some good differential properties. A damped Newton type method was presented based on it. Global and local superlinear/ quadratic convergence results were obtained under mild conditions, and the finite termination property was also shown for the linear BVIs. Numerical results suggest that the method is efficient and promising.展开更多
非刚性点集配准是计算机视觉和模式识别领域的基础研究问题,现今的非刚性点集配准算法在存在大量离群点、噪声、点集对应关系缺失、旋转和形变情况下,不能非常准确地评估出两个点集间的对应关系.本文通过交替执行点集对应关系评估和空...非刚性点集配准是计算机视觉和模式识别领域的基础研究问题,现今的非刚性点集配准算法在存在大量离群点、噪声、点集对应关系缺失、旋转和形变情况下,不能非常准确地评估出两个点集间的对应关系.本文通过交替执行点集对应关系评估和空间转换更新两个步骤来逐步恢复点集间一一对应关系.在对应关系评估步骤,首先本文基于有限重尾学生t分布隐变量混合模型(student-t distribution Latent Mixture Model,简称TLMM)构造变分贝叶斯层次概率模型(Variational Bayes Hierarchical Probability Model,简称VBHPM)并将其分为对应关系评估组件和离群点聚合组件,分别用来评估点集间对应关系和聚合离群点,同时使用贝叶斯线性回归方法来抵抗噪声的干扰.其次本文加入Dirichlet先验分布来动态调节模型的混合比例,为对应关系缺失的点分配较小的混合比例以保持点集结构的稳定性.在空间转换更新步骤,本文基于变分贝叶斯(Variational Bayes,简称VB)框架来迭代更新模型参数,并提出树状平均场因式分解方法来维持模型参数间的依赖关系,以获得更紧致的变分下界.此外,本文提出自适应全局-局部约束策略来维持点集间结构的稳定性,抵抗形变和旋转影响的同时实现从局部到全局的约束过程.最后,本文采用了双阶段先验退火方案,在退火过程中使用Gamma先验分布来动态调节精度,实现由粗到精的配准过程.在实验部分,本文不仅测试了VBHPM的性能,而且展示了点集和图像配准的结果,并与当前流行的13种算法进行了比较,VBHPM皆能展现较准确的配准结果和较高的精度.展开更多
基金Project supported by the Teaching and Research Award Program for the Outstanding YoungTeachers in Higher Education Institutes of Munistry of Education, P.R.China
文摘By introducing a smooth merit function for the median function, a new smooth merit function for box constrained variational inequalities (BVIs) was constructed. The function is simple and has some good differential properties. A damped Newton type method was presented based on it.Global and local superlinear/quadratic convergence results were obtained under mild conditions, and the finite termination property was also shown for the linear BVIs. Numerical results suggest that the method is efficient and promising.
文摘By introducing a smooth merit function for the median function, a new smooth merit function for box constrained variational inequalities （BVIs） was constructed. The function is simple and has some good differential properties. A damped Newton type method was presented based on it. Global and local superlinear/ quadratic convergence results were obtained under mild conditions, and the finite termination property was also shown for the linear BVIs. Numerical results suggest that the method is efficient and promising.
文摘非刚性点集配准是计算机视觉和模式识别领域的基础研究问题,现今的非刚性点集配准算法在存在大量离群点、噪声、点集对应关系缺失、旋转和形变情况下,不能非常准确地评估出两个点集间的对应关系.本文通过交替执行点集对应关系评估和空间转换更新两个步骤来逐步恢复点集间一一对应关系.在对应关系评估步骤,首先本文基于有限重尾学生t分布隐变量混合模型(student-t distribution Latent Mixture Model,简称TLMM)构造变分贝叶斯层次概率模型(Variational Bayes Hierarchical Probability Model,简称VBHPM)并将其分为对应关系评估组件和离群点聚合组件,分别用来评估点集间对应关系和聚合离群点,同时使用贝叶斯线性回归方法来抵抗噪声的干扰.其次本文加入Dirichlet先验分布来动态调节模型的混合比例,为对应关系缺失的点分配较小的混合比例以保持点集结构的稳定性.在空间转换更新步骤,本文基于变分贝叶斯(Variational Bayes,简称VB)框架来迭代更新模型参数,并提出树状平均场因式分解方法来维持模型参数间的依赖关系,以获得更紧致的变分下界.此外,本文提出自适应全局-局部约束策略来维持点集间结构的稳定性,抵抗形变和旋转影响的同时实现从局部到全局的约束过程.最后,本文采用了双阶段先验退火方案,在退火过程中使用Gamma先验分布来动态调节精度,实现由粗到精的配准过程.在实验部分,本文不仅测试了VBHPM的性能,而且展示了点集和图像配准的结果,并与当前流行的13种算法进行了比较,VBHPM皆能展现较准确的配准结果和较高的精度.
