会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 1. 发明授权
    • System and method for image and video segmentation by anisotropic kernel mean shift
    • 通过各向异性核平均偏移的图像和视频分割的系统和方法
    • US07397948B1
    • 2008-07-08
    • US10796736
    • 2004-03-08
    • Michael CohenBo ThiessonYing-Qing XuJue Wang
    • Michael CohenBo ThiessonYing-Qing XuJue Wang
    • G06K9/00
    • G06K9/4652G06T7/11
    • Mean shift is a nonparametric estimator of density which has been applied to image and video segmentation. Traditional mean shift based segmentation uses a radially symmetric kernel to estimate local density, which is not optimal in view of the often structured nature of image and more particularly video data. The system and method of the invention employs an anisotropic kernel mean shift in which the shape, scale, and orientation of the kernels adapt to the local structure of the image or video. The anisotropic kernel is decomposed to provide handles for modifying the segmentation based on simple heuristics. Experimental results show that the anisotropic kernel mean shift outperforms the original mean shift on image and video segmentation in the following aspects: 1) it gets better results on general images and video in a smoothness sense; 2) the segmented results are more consistent with human visual saliency; and 3) the system and method is robust to initial parameters.
    • 平均偏移是已经应用于图像和视频分割的密度的非参数估计器。 传统的基于平均移位的分割使用径向对称的核来估计局部密度,鉴于图像的经常结构化的特性,更特别是视频数据,这是非最优的。 本发明的系统和方法采用各向异性核平均移位,其中内核的形状,尺度和取向适应于图像或视频的局部结构。 各向异性核被分解以提供用于基于简单启发式修改分割的句柄。 实验结果表明,各向异性核平均偏移在以下几个方面优于原始平均偏移图像和视频分割:1)在平滑度方面对一般图像和视频获得更好的结果; 2)分段结果与人类视觉显着性更为一致; 和3)系统和方法对初始参数是鲁棒的。
    • 8. 发明申请
    • Automated face enhancement
    • 自动脸部增强
    • US20070189627A1
    • 2007-08-16
    • US11353799
    • 2006-02-14
    • Michael CohenJue Wang
    • Michael CohenJue Wang
    • G06K9/40G06K9/00
    • G06K9/00228
    • An automated face enhancement system and process which can automatically improve faces in videos or other images by applying cosmetic effects, given only a small amount of user interaction for initialization. For an input video, the system will first track the face and eye locations, and classify pixels in the face into different facial components based on local color models. The classification results of different frames are temporally smoothed to ensure temporal coherence. A set of cosmetic filters are then applied to different facial components.
    • 一种自动化的面部增强系统和过程,可以通过应用美容效果来自动改善视频或其他图像中的脸部,只给予少量用户交互进行初始化。 对于输入视频,系统将首先跟踪脸部和眼睛的位置,并根据本地颜色模型将脸部像素分类为不同的面部组件。 不同帧的分类结果在时间上平滑,以确保时间一致性。 然后将一套美容过滤器应用于不同的面部组件。