随着图像数据的爆炸性增长,图像处理变得越来越重要。显著性目标检测是图像处理的重要研究方向之一,目前已采用多种研究方法进行显著性目标检测,但是传统的显著性检测方法所使用的低级特征对于复杂场景并不健壮。全卷积神经网络在图像...随着图像数据的爆炸性增长,图像处理变得越来越重要。显著性目标检测是图像处理的重要研究方向之一,目前已采用多种研究方法进行显著性目标检测,但是传统的显著性检测方法所使用的低级特征对于复杂场景并不健壮。全卷积神经网络在图像处理中表现出良好的性能,但存在目标显著性检测边界模糊等不足。为解决边界模糊等问题,该模型采用了一种具有跳跃连接的全卷积神经网络,以及5个不同膨胀率的空洞卷积按照一定规则组成的ESP模块,在全卷积神经网络的基础上采用ESP模块和不同的跳跃连接方式,以获取更多的低级特征来精确多目标显著对象的边界。实验中运用MIT Scene Parsing数据集训练和测试模型,通过与相关模型在精度和MIOU上的比较结果表明,在保证模型的处理时间未增加的同时,经过改进的全卷积神经网络的检测具有更高的准确度以及更精确的边界信息。展开更多
As a kind of weak-path dependent options, barrier options are an important kind of exotic options. Because the pricing formula for pricing barrier options with discrete observations cannot avoid computing a high dimen...As a kind of weak-path dependent options, barrier options are an important kind of exotic options. Because the pricing formula for pricing barrier options with discrete observations cannot avoid computing a high dimensional integral, numerical calculation is time-consuming. In the current studies, some scholars just obtained theoretical derivation, or gave some simulation calculations. Others impose underlying assets on some strong assumptions, for example, a lot of calculations are based on the Black-Scholes model. This thesis considers Merton jump diffusion model as the basic model to derive the pricing formula of discrete double barrier option;numerical calculation method is used to approximate the continuous convolution by calculating discrete convolution. Then we compare the results of theoretical calculation with simulation results by Monte Carlo method, to verify their efficiency and accuracy. By comparing the results of degeneration constant parameter model with the results of previous models we verified the calculation method is correct indirectly. Compared with the Monte Carlo simulation method, the numerical results are stable. Even if we assume the simulation results are accurate, the time consumed by the numerical method to achieve the same accuracy is much less than the Monte Carlo simulation method.展开更多
文摘随着图像数据的爆炸性增长,图像处理变得越来越重要。显著性目标检测是图像处理的重要研究方向之一,目前已采用多种研究方法进行显著性目标检测,但是传统的显著性检测方法所使用的低级特征对于复杂场景并不健壮。全卷积神经网络在图像处理中表现出良好的性能,但存在目标显著性检测边界模糊等不足。为解决边界模糊等问题,该模型采用了一种具有跳跃连接的全卷积神经网络,以及5个不同膨胀率的空洞卷积按照一定规则组成的ESP模块,在全卷积神经网络的基础上采用ESP模块和不同的跳跃连接方式,以获取更多的低级特征来精确多目标显著对象的边界。实验中运用MIT Scene Parsing数据集训练和测试模型,通过与相关模型在精度和MIOU上的比较结果表明,在保证模型的处理时间未增加的同时,经过改进的全卷积神经网络的检测具有更高的准确度以及更精确的边界信息。
文摘As a kind of weak-path dependent options, barrier options are an important kind of exotic options. Because the pricing formula for pricing barrier options with discrete observations cannot avoid computing a high dimensional integral, numerical calculation is time-consuming. In the current studies, some scholars just obtained theoretical derivation, or gave some simulation calculations. Others impose underlying assets on some strong assumptions, for example, a lot of calculations are based on the Black-Scholes model. This thesis considers Merton jump diffusion model as the basic model to derive the pricing formula of discrete double barrier option;numerical calculation method is used to approximate the continuous convolution by calculating discrete convolution. Then we compare the results of theoretical calculation with simulation results by Monte Carlo method, to verify their efficiency and accuracy. By comparing the results of degeneration constant parameter model with the results of previous models we verified the calculation method is correct indirectly. Compared with the Monte Carlo simulation method, the numerical results are stable. Even if we assume the simulation results are accurate, the time consumed by the numerical method to achieve the same accuracy is much less than the Monte Carlo simulation method.
