generalized local extremum

Study on local extremum of object function in mutual information-based image registration;

基于互信息图像配准中的局部极值问题研究

The difficulty of optimization caused by local extremum is the key problem of the algorithm.

由局部极值导致的寻优困难是困扰该算法的核心问题,混合优化算法成功地解决了互信息函数的寻优问题,但延长了配准时间。

It can not only overcome the disadvantage of easily getting into the local extremum in the later evolution period,but also keep the rapidity of the previous period.

所提出的算法将粒子群优化算法和混沌算法相结合,既摆脱了算法搜索后期易陷入局部极值点的缺点,同时又保持了前期搜索的快速性。

Overcome of local maximum in mutual information-based image registration;

基于互信息量图像配准中目标函数局部极值的克服

The cause of local maximum of object functions image registration was analyzed based on mutual information, and an optimization strategy by using simulated annealing-simplex method proposed.

分析了在基于互信息方法的图像配准中,目标函数产生局部极值的原因,提出以模拟退火单纯形法作为优化策略,该方法利用了单纯形法的一种修改后的形式作为模拟退火中随机变化的发生器。

That means local extrema of histograms are sensitive to ±1 embedding method.

这意味着图像直方图及其小波直方图的局部极值对嵌入方法比较敏感。

This paper gives the sufficient conditions for the blow-up of the solution of the Cauchy problem for a nonlinear hyperbolic equation in finite time and proves the existence and uniqueness of the local generalized solution.

给出一类非线性双曲型方程初值问题解爆破的充分条件,并且证明问题局部广义解的存在性和唯一性。