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1)  Artifact reduction
伪影消除
2)  artifact removing
伪影去除
3)  Anifacts Remval
伪迹消除
4)  shadow elimination
阴影消除
1.
Detection of moving targets based on Kalman filter and shadow elimination
基于Kalman滤波和阴影消除的运动目标检测
2.
First,this paper analyse the optical characteristic of cast shadows,then propose a shadow elimination method based on chrominance distortion and local Kullback-Leibler divergence.
根据阴影区域特有的光学属性,提出了一种基于色度畸变和局部交叉熵的阴影消除方法。
3.
In this paper, several key technologies in gait recognition are researched, including the shadow elimination of moving human in the phase of pretreatment, the solving of occlusion in multi-human tracking, the extraction of gait features and the gait recognition.
本文主要是对步态识别中的几个关键技术进行研究,其中包括步态识别预处理中的运动人体的阴影消除、多人跟踪中遮挡问题的解决以及步态特征的提取和步态的识别。
5)  shadow removing
阴影消除
6)  shadow removal
阴影消除
1.
Another method of shadow removal based on edge information is presented too, which is independent of the direction of shadow, and can effectively remove the cast shadow of a moving object.
针对前景检测时出现的阴影,提出基于边缘信息的阴影消除算法。
2.
The number of mixture components of GMM is estimated according to the frequency of pixel value changes,and the performance of GMM can be effectively improved with the modified background learning and update new distribution generation rule and shadow removal based on morphological reconstruction.
针对固定场景监控中复杂背景、光照变化、阴影等影响视频分割的问题,提出了一种有效的混合高斯模型的自适应背景更新算法,各像素点根据其像素值出现的混乱程度采取不同个数的高斯分布描述,通过对背景模型的学习与更新、高斯分布生成准则等方面的改进和优化,采用基于形态学重构的阴影消除方法使得前景目标分割的性能得到了有效地提高。
3.
After analyzing the shortages of Center/Surround Retinex(CSR) algorithm and shadow removal approaches based on Retinex in existence,an Anisotropic Diffusion Center/Surround Retinex(ADCSR) is presented to solve the problem.
通过分析中心/环绕Retinex算法(center/surround Retinex,CSR)以及现有的基于Retinex的阴影消除算法处理阴影图像的不足,提出了一种各向异性中心/环绕Retinex算法(anisotropic diffusion center/surround Retinex,ADCSR),该算法融入了基于偏微分方程(PDE)的各向异性扩散,并根据算法特点提出了基于"边界性"(edge degree,ED)的各向异性扩散方案,避免了梯度门限等参数选择的困难,在消除阴影的效果上和运算效率上都取得了令人满意的结果。
补充资料:部分容积效应伪影


部分容积效应伪影


  超声学术语。声束宽度在非聚焦区可达几毫米至1cm以上,当其遇到小于声束切面厚度的病灶时,在声像图上出现病灶区与其周围区回声相互重叠的图像而造成的伪影。这可导致例如小囊肿内部存在有细小光点而误认为实质性肿物的分析错误。
  
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