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    • 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。
    • 4. 发明授权
    • Stylization of video
    • 视频风格化
    • US07657060B2
    • 2010-02-02
    • US10814851
    • 2004-03-31
    • Michael F. CohenYing-Qing XuHeung-Yeung ShumJue Wang
    • Michael F. CohenYing-Qing XuHeung-Yeung ShumJue Wang
    • G06K9/00
    • G11B27/034G06K9/00711G06T15/02H04N5/262
    • The techniques and mechanisms described herein are directed to a system for stylizing video, such as interactively transforming video to a cartoon-like style. Briefly stated, the techniques include determining a set of volumetric objects within a video, each volumetric object being a segment. Mean shift video segmentation may be used for this step. With that segmentation information, the technique further includes indicating on a limited number of keyframes of the video how segments should be merged into a semantic region. Finally, a contiguous volume is created by interpolating between keyframes by a mean shift constrained interpolation technique to propagate the semantic regions between keyframes.
    • 这里描述的技术和机制针对用于对视频进行风格化的系统,诸如将视频交互地变换成卡通样式。 简而言之,技术包括确定视频内的一组体积对象,每个体积对象是一段。 平均移位视频分割可用于该步骤。 利用该分割信息,该技术还包括在片段的有限数量的关键帧上指示片段如何被合并到语义区域中。 最后,通过平均偏移约束插值技术在关键帧之间进行内插以在关键帧之间传播语义区域来创建连续体积。
    • 5. 发明授权
    • Stylization of video
    • 视频风格化
    • US07450758B2
    • 2008-11-11
    • US11942606
    • 2007-11-19
    • Michael F. CohenYing-Qing XuHeung-Yeung ShumJue Wang
    • Michael F. CohenYing-Qing XuHeung-Yeung ShumJue Wang
    • G06K9/00
    • G11B27/034G06K9/00711G06T15/02H04N5/262
    • The techniques and mechanisms described herein are directed to a system for stylizing video, such as interactively transforming video to a cartoon-like style. Briefly stated, the techniques include determining a set of volumetric objects within a video, each volumetric object being a segment. Mean shift video segmentation may be used for this step. With that segmentation information, the technique further includes indicating on a limited number of keyframes of the video how segments should be merged into a semantic region. Finally, a contiguous volume is created by interpolating between keyframes by a mean shift constrained interpolation technique to propagate the semantic regions between keyframes.
    • 这里描述的技术和机制针对用于对视频进行风格化的系统,诸如将视频交互地变换成卡通样式。 简而言之,技术包括确定视频内的一组体积对象,每个体积对象是一段。 平均移位视频分割可用于该步骤。 利用该分割信息,该技术还包括在片段的有限数量的关键帧上指示片段如何被合并到语义区域中。 最后,通过平均偏移约束插值技术在关键帧之间进行内插以在关键帧之间传播语义区域来创建连续体积。
    • 6. 发明授权
    • Learning-based system and process for synthesizing cursive handwriting
    • 基于学习的系统和合成草书手写的过程
    • US07227993B2
    • 2007-06-05
    • US10353102
    • 2003-01-27
    • Ying-Qing XuHeung-Yeung ShumJue WangChenyu Wu
    • Ying-Qing XuHeung-Yeung ShumJue WangChenyu Wu
    • G06K9/34G06K9/18G06K9/00G06K9/46
    • G06F3/04883G06K9/222G06T11/203
    • A process and system for modeling, learning and synthesizing cursive handwriting in a user's personal handwriting style. The handwriting synthesis system and process described herein addresses the problem of learning the personal handwriting style of a user based on limited handwriting samples and producing novel scripts of the same style. The handwriting synthesis process includes segmenting handwriting samples into individual characters using a two-level writer-independent segmentation process, aligning samples of the same character into a common coordinate frame, and learning and modeling the individual character. Synthesis of handwriting is performed by generating individual letters from the models and concatenating the letters using a conditional sampling algorithm. The result is a smooth and fluid connection between letters that successfully mimics the personal handwriting style of a user.
    • 一种在用户个人手写风格中建模,学习和综合草书手写的过程和系统。 本文描述的手写合成系统和过程解决了基于有限的手写样本学习用户的个人手写风格并产生相同风格的新颖的脚本的问题。 手写合成过程包括使用两级写入器独立分割处理将手写样本分割成单个字符,将相同字符的样本对齐到公共坐标系中,并且学习和建模个体角色。 通过从模型生成单个字母并使用条件抽样算法连接字母来执行手写的合成。 结果是成功模仿用户个人手写风格的字母之间平滑流畅的连接。
    • 10. 发明授权
    • Automated face enhancement
    • 自动脸部增强
    • US07634108B2
    • 2009-12-15
    • US11353799
    • 2006-02-14
    • Michael CohenJue Wang
    • Michael CohenJue Wang
    • G06K9/00G06K9/40
    • 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。