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1. 发明授权

US08243071B2 Modeling and rendering of heterogeneous translucent materials using the diffusion equation 有权
标题翻译：使用扩散方程建模和渲染异质半透明材料
公开(公告)号：US08243071B2
公开(公告)日：2012-08-14
申请号：US12072925
申请日：2008-02-29
申请人： Jiaping Wang , Xin Tong , Stephen S. Lin , Baining Guo , Heung-Yeung Shum , Zhouchen Lin
发明人： Jiaping Wang , Xin Tong , Stephen S. Lin , Baining Guo , Heung-Yeung Shum , Zhouchen Lin
IPC分类号： G06T15/00 , G06T15/60 , G06K9/00
CPC分类号： G06T15/506 , G01N21/47 , G01N21/4738 , G06T15/50
摘要： An exemplary method includes providing image data for an illuminated physical sample of a heterogeneous translucent material, determining one or more material properties of the material based in part on a diffusion equation where one of the material properties is a diffusion coefficient for diffusion of radiation in the material and where the determining includes a regularization term for the diffusion coefficient, mapping the one or more material properties to a virtual object volume, assigning virtual illumination conditions to the virtual object volume, and rendering the virtual object volume using the virtual illumination conditions as a boundary condition for a system of diffusion equations of the virtual object volume. Other methods, devices and systems are also disclosed.
摘要翻译：一种示例性方法包括提供用于异质半透明材料的照明物理样品的图像数据，部分地基于扩散方程来确定材料的一种或多种材料性质，其中材料性质之一是用于辐射的扩散的扩散系数材料，并且其中确定包括扩散系数的正则化术语，将一个或多个材料属性映射到虚拟对象体积，将虚拟照明条件分配给虚拟对象体积，以及使用虚拟照明条件将虚拟对象体积呈现为虚拟物体体积扩散方程组的边界条件。还公开了其它方法，装置和系统。

2. 发明授权

US08730240B2 Modeling and rendering of heterogeneous translucent materals using the diffusion equation 有权
标题翻译：使用扩散方程建模和渲染异质半透明材料
公开(公告)号：US08730240B2
公开(公告)日：2014-05-20
申请号：US13584158
申请日：2012-08-13
申请人： Jiaping Wang , Xin Tong , Stephen S. Lin , Baining Guo , Heung-Yeung Shum , Zhouchen Lin
发明人： Jiaping Wang , Xin Tong , Stephen S. Lin , Baining Guo , Heung-Yeung Shum , Zhouchen Lin
IPC分类号： G06T15/50 , G06T15/60 , G01N21/47
CPC分类号： G06T15/506 , G01N21/47 , G01N21/4738 , G06T15/50
摘要： An exemplary method includes providing image data for an illuminated physical sample of a heterogeneous translucent material, determining one or more material properties of the material based in part on a diffusion equation where one of the material properties is a diffusion coefficient for diffusion of radiation in the material and where the determining includes a regularization term for the diffusion coefficient, mapping the one or more material properties to a virtual object volume, assigning virtual illumination conditions to the virtual object volume, and rendering the virtual object volume using the virtual illumination conditions as a boundary condition for a system of diffusion equations of the virtual object volume. Other methods, devices and systems are also disclosed.
摘要翻译：一种示例性方法包括提供用于异质半透明材料的照明物理样品的图像数据，部分地基于扩散方程来确定材料的一种或多种材料性质，其中材料性质之一是用于辐射的扩散的扩散系数材料，并且其中确定包括扩散系数的正则化术语，将一个或多个材料属性映射到虚拟对象体积，将虚拟照明条件分配给虚拟对象体积，以及使用虚拟照明条件将虚拟对象体积呈现为虚拟物体体积扩散方程组的边界条件。还公开了其它方法，装置和系统。

3. 发明申请

US20120306878A1 Modeling and Rendering of Heterogeneous Translucent Materals Using The Diffusion Equation 有权
标题翻译：使用扩散方程建模和渲染异质半透明材料
公开(公告)号：US20120306878A1
公开(公告)日：2012-12-06
申请号：US13584158
申请日：2012-08-13
申请人： Jiaping Wang , Xin Tong , Stephen S. Lin , Baining Guo , Heung-Yeung Shum , Zhouchen Lin
发明人： Jiaping Wang , Xin Tong , Stephen S. Lin , Baining Guo , Heung-Yeung Shum , Zhouchen Lin
IPC分类号： G06T15/50
CPC分类号： G06T15/506 , G01N21/47 , G01N21/4738 , G06T15/50
摘要： An exemplary method includes providing image data for an illuminated physical sample of a heterogeneous translucent material, determining one or more material properties of the material based in part on a diffusion equation where one of the material properties is a diffusion coefficient for diffusion of radiation in the material and where the determining includes a regularization term for the diffusion coefficient, mapping the one or more material properties to a virtual object volume, assigning virtual illumination conditions to the virtual object volume, and rendering the virtual object volume using the virtual illumination conditions as a boundary condition for a system of diffusion equations of the virtual object volume. Other methods, devices and systems are also disclosed.
摘要翻译：一种示例性方法包括提供用于异质半透明材料的照明物理样品的图像数据，部分地基于扩散方程来确定材料的一种或多种材料性质，其中材料性质之一是用于辐射的扩散的扩散系数材料，并且其中确定包括扩散系数的正则化术语，将一个或多个材料属性映射到虚拟对象体积，将虚拟照明条件分配给虚拟对象体积，以及使用虚拟照明条件将虚拟对象体积呈现为虚拟物体体积扩散方程组的边界条件。还公开了其它方法，装置和系统。

4. 发明申请

US20090219287A1 Modeling and rendering of heterogeneous translucent materials using the diffusion equation 有权
标题翻译：使用扩散方程建模和渲染异质半透明材料
公开(公告)号：US20090219287A1
公开(公告)日：2009-09-03
申请号：US12072925
申请日：2008-02-29
申请人： Jiaping Wang , Xin Tong , Stephen S. Lin , Baining Guo , Heung-Yeung Shum , Zhouchen Lin
发明人： Jiaping Wang , Xin Tong , Stephen S. Lin , Baining Guo , Heung-Yeung Shum , Zhouchen Lin
IPC分类号： G06T15/50 , G06F3/048
CPC分类号： G06T15/506 , G01N21/47 , G01N21/4738 , G06T15/50
摘要： An exemplary method includes providing image data for an illuminated physical sample of a heterogeneous translucent material, determining one or more material properties of the material based in part on a diffusion equation where one of the material properties is a diffusion coefficient for diffusion of radiation in the material and where the determining includes a regularization term for the diffusion coefficient, mapping the one or more material properties to a virtual object volume, assigning virtual illumination conditions to the virtual object volume, and rendering the virtual object volume using the virtual illumination conditions as a boundary condition for a system of diffusion equations of the virtual object volume. Other methods, devices and systems are also disclosed.
摘要翻译：一种示例性方法包括提供用于异质半透明材料的照明物理样品的图像数据，部分地基于扩散方程来确定材料的一种或多种材料性质，其中材料性质之一是用于辐射的扩散的扩散系数材料，并且其中确定包括扩散系数的正则化术语，将一个或多个材料属性映射到虚拟对象体积，将虚拟照明条件分配给虚拟对象体积，以及使用虚拟照明条件将虚拟对象体积呈现为虚拟物体体积扩散方程组的边界条件。还公开了其它方法，装置和系统。

5. 发明授权

US08406556B2 Light transport reconstruction from sparsely captured images 有权
标题翻译：从稀疏捕获的图像的光传输重建
公开(公告)号：US08406556B2
公开(公告)日：2013-03-26
申请号：US12797859
申请日：2010-06-10
申请人： Jiaping Wang , Yue Dong , Xin Tong , Zhouchen Lin , Baining Guo
发明人： Jiaping Wang , Yue Dong , Xin Tong , Zhouchen Lin , Baining Guo
IPC分类号： G06K9/40
CPC分类号： G06T15/50
摘要： A “Scene Re-Lighter” provides various techniques for using an automatically reconstructed light transport matrix derived from a sparse sampling of images to provide various combinations of complex light transport effects in images, including caustics, complex occlusions, inter-reflections, subsurface scattering, etc. More specifically, the Scene Re-Lighter reconstructs the light transport matrix from a relatively small number of acquired images using a “Kernel Nyström” based technique adapted for low rank matrices constructed from sparsely sampled images. A “light transport kernel” is incorporated into the Nyström method to exploit nonlinear coherence in the light transport matrix. Further, an adaptive process is used to efficiently capture the sparsely sampled images from a scene. The Scene Re-Lighter is capable of achieving good reconstruction of the light transport matrix with only few hundred images to produce high quality relighting results. Further, the Scene Re-Lighter is also effective for modeling scenes with complex lighting effects and occlusions.
摘要翻译：场景再打火机提供了使用从图像稀疏采样得到的自动重建光传输矩阵的各种技术，以提供图像中复杂光传输效应的各种组合，包括焦散，复杂遮挡，相互反射，地下散射等。更具体地说，场景重新点亮器使用适用于由稀疏采样图像构成的低秩矩阵的基于内核Nyström的技术，从相对较少数量的获取图像重构光传输矩阵。光传输核被并入Nyström方法，以利用光传输矩阵中的非线性相干性。此外，使用自适应处理来有效地从场景捕获稀疏采样的图像。场景重新打火机能够通过仅仅几百张图像实现光传输矩阵的良好重建，从而产生高品质的重视效果。此外，场景重新打火机对于对具有复杂的照明效果和遮挡的场景进行建模也是有效的。

6. 发明申请

US20110304745A1 LIGHT TRANSPORT RECONSTRUCTION FROM SPARSELY CAPTURED IMAGES 有权
标题翻译：轻量运输重建从小型捕获图像
公开(公告)号：US20110304745A1
公开(公告)日：2011-12-15
申请号：US12797859
申请日：2010-06-10
申请人： Jiaping Wang , Yue Dong , Zhouchen Lin , Xin Tong , Baining Guo
发明人： Jiaping Wang , Yue Dong , Zhouchen Lin , Xin Tong , Baining Guo
IPC分类号： H04N5/235 , G06K9/40
CPC分类号： G06T15/50
摘要： A “Scene Re-Lighter” provides various techniques for using an automatically reconstructed light transport matrix derived from a sparse sampling of images to provide various combinations of complex light transport effects in images, including caustics, complex occlusions, inter-reflections, subsurface scattering, etc. More specifically, the Scene Re-Lighter reconstructs the light transport matrix from a relatively small number of acquired images using a “Kernel Nyström” based technique adapted for low rank matrices constructed from sparsely sampled images. A “light transport kernel” is incorporated into the Nyström method to exploit nonlinear coherence in the light transport matrix. Further, an adaptive process is used to efficiently capture the sparsely sampled images from a scene. The Scene Re-Lighter is capable of achieving good reconstruction of the light transport matrix with only few hundred images to produce high quality relighting results. Further, the Scene Re-Lighter is also effective for modeling scenes with complex lighting effects and occlusions.
摘要翻译： “场景再打火机”提供了各种技术，用于使用从图像的稀疏采样导出的自动重建的光传输矩阵，以提供图像中复杂光传输效应的各种组合，包括焦散，复杂遮挡，相互反射，地下散射，等等。更具体地，场景重新点亮器使用适用于由稀疏采样图像构成的低秩矩阵的基于“内核Nyström”的技术，从相对较少数量的获取图像重构光传输矩阵。将“光传输核”纳入Nyström方法以利用光传输矩阵中的非线性相干性。此外，使用自适应处理来有效地从场景捕获稀疏采样的图像。场景重新打火机能够通过仅仅几百张图像实现光传输矩阵的良好重建，从而产生高品质的重视效果。此外，场景重新打火机对于对具有复杂的照明效果和遮挡的场景进行建模也是有效的。

7. 发明申请

US20060262973A1 Detecting doctored images using camera response normality and consistency 有权
公开(公告)号：US20060262973A1
公开(公告)日：2006-11-23
申请号：US11132865
申请日：2005-05-19
申请人： Zhouchen Lin , Rongrong Wang , Xiaoou Tang , Heung-Yeung Shum
发明人： Zhouchen Lin , Rongrong Wang , Xiaoou Tang , Heung-Yeung Shum
IPC分类号： G06K9/62 , G06K9/00 , G06K15/00
CPC分类号： G06K9/00 , G06K9/00899
摘要： Embodiments of the invention determine whether an image has been altered. Sets of patches are selected in the image, and corresponding inverse response functions are provided to a support vector machine (SVM). The support vector machine is trained with exemplary normal and abnormal inverse response functions. Once trained, the support vector machine analyzes inverse response functions corresponding to a suspected image. The support vector machine determines if the inverse response functions are normal or abnormal by analyzing a set of features. In one embodiment, features include measures for monotonic characteristics, fluctuation characteristics, and divergence characteristics of the red, green, and blue components of a tuple. Each tuple of inverse response functions is associated with a set of patches selected in the image.

8. 发明授权

US07505606B2 Detecting doctored images using camera response normality and consistency 有权
标题翻译：使用相机响应的正常性和一致性来检测图像
公开(公告)号：US07505606B2
公开(公告)日：2009-03-17
申请号：US11132865
申请日：2005-05-19
申请人： Zhouchen Lin , Rongrong Wang , Xiaoou Tang , Heung-Yeung Shum
发明人： Zhouchen Lin , Rongrong Wang , Xiaoou Tang , Heung-Yeung Shum
IPC分类号： G06K9/00
CPC分类号： G06K9/00 , G06K9/00899
摘要： Embodiments of the invention determine whether an image has been altered. Sets of patches are selected in the image, and corresponding inverse response functions are provided to a support vector machine (SVM). The support vector machine is trained with exemplary normal and abnormal inverse response functions. Once trained, the support vector machine analyzes inverse response functions corresponding to a suspected image. The support vector machine determines if the inverse response functions are normal or abnormal by analyzing a set of features. In one embodiment, features include measures for monotonic characteristics, fluctuation characteristics, and divergence characteristics of the red, green, and blue components of a tuple. Each tuple of inverse response functions is associated with a set of patches selected in the image.
摘要翻译：本发明的实施例确定图像是否已被改变。在图像中选择一组补丁，并将相应的反应函数提供给支持向量机（SVM）。支持向量机用示例性正常和异常的反应函数进行训练。训练后，支持向量机分析与疑似图像相对应的反应响应函数。支持向量机通过分析一组特征来确定逆响应函数是正常还是异常。在一个实施例中，特征包括用于元组的红，绿和蓝分量的单调特性，波动特性和发散特性的度量。反向响应函数的每个元组与在图像中选择的一组补丁相关联。

9. 发明申请

US20100262643A1 COMPUTING MINIMAL POLYNOMIALS 有权
标题翻译：计算最小多边形
公开(公告)号：US20100262643A1
公开(公告)日：2010-10-14
申请号：US12422315
申请日：2009-04-13
申请人： Xu Yang , Zhouchen Lin , Sijun Liu , Tianjun Ye , Dongmei Zhang
发明人： Xu Yang , Zhouchen Lin , Sijun Liu , Tianjun Ye , Dongmei Zhang
IPC分类号： G06F7/38 , G06F7/552
CPC分类号： G06F7/724 , G06F7/12 , G06F7/16
摘要： Described is a technology, such as implemented in a computational software program, by which a minimal polynomial is efficiently determined for a radical expression over the ring Z of integer numbers or the ring Q of rational numbers. The levels of the radical are grouped into a level permutation group that is used to find a level permutation set. An annihilation polynomial is found based upon the level permutation set. The annihilation polynomial is factored, and a selection mechanism selects the minimal polynomial based upon the annihilation polynomial's factors.
摘要翻译：描述了一种技术，例如在计算软件程序中实现的技术，通过该技术，对于整数的环Z或有理数的环Q，对于根基表达式有效地确定最小多项式。激进的级别被分组成用于找到级别置换集合的级别置换组。湮没多项式是基于层次排列集合找到的。湮灭多项式被考虑，选择机制根据湮灭多项式的因素选择最小多项式。

10. 发明申请

US20100080450A1 CLASSIFICATION VIA SEMI-RIEMANNIAN SPACES 有权
标题翻译：通过SEMI-RIEMANNIAN SPACES分类
公开(公告)号：US20100080450A1
公开(公告)日：2010-04-01
申请号：US12242421
申请日：2008-09-30
申请人： Deli Zhao , Zhouchen Lin , Xiaoou Tang
发明人： Deli Zhao , Zhouchen Lin , Xiaoou Tang
IPC分类号： G06K9/62
CPC分类号： G06K9/6234 , G06K9/6252
摘要： Described is using semi-Riemannian geometry in supervised learning to learn a discriminant subspace for classification, e.g., labeled samples are used to learn the geometry of a semi-Riemannian submanifold. For a given sample, the K nearest classes of that sample are determined, along with the nearest samples that are in other classes, and the nearest samples in that sample's same class. The distances between these samples are computed, and used in computing a metric matrix. The metric matrix is used to compute a projection matrix that corresponds to the discriminant subspace. In online classification, as a new sample is received, it is projected into a feature space by use of the projection matrix and classified accordingly.
摘要翻译：描述了在监督学习中使用半黎曼几何学习学习用于分类的判别子空间，例如，标记的样本用于学习半黎曼子流形歧管的几何形状。对于给定的样本，该样本的K个最近类别以及其他类别中最近的样本以及该样本同一类中最近的样本进行确定。计算这些样本之间的距离，并用于计算度量矩阵。度量矩阵用于计算与判别子空间对应的投影矩阵。在线分类中，作为收到的新样本，通过使用投影矩阵将其投影到特征空间中并进行分类。

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