公开(公告)号：US07986820B2

公开(公告)日：2011-07-26

申请号：US10087409

申请日：2001-10-19

申请人： Baback Moghaddam

发明人： Baback Moghaddam

IPC分类号： G06K9/00 ,  G06K9/46 ,  G06K9/74 ,  G08K9/40

CPC分类号： G06K9/00087

摘要： Features are extracted from a test and reference image to generate a test and reference record. Each feature has a location, and orientation, and furthermore, the features of the reference records also have associated weights. The features of the test record are approximately aligned with the features of the reference record. Then, differences between the locations and orientations of the features of the reference record and the features of the test record are measured, and the weights of all features of the reference record that are less than a predetermined difference when compared with the features of the test record are summed to determine a similarity score that the test record matches the reference record.

摘要翻译： 从测试和参考图像中提取特征以产生测试和参考记录。 每个特征具有位置和方向，此外，参考记录的特征也具有相关联的权重。 测试记录的特征大致与参考记录的特征对齐。 然后，测量参考记录的特征的位置和取向与测试记录的特征之间的差异，并且与测试的特征相比，参考记录的所有特征的权重小于预定的差异 记录相加以确定测试记录与参考记录匹配的相似性得分。

公开(公告)号：US07609859B2

公开(公告)日：2009-10-27

申请号：US11152528

申请日：2005-06-14

申请人： Jinho Lee ,  Baback Moghaddam ,  Hanspeter Pfister ,  Raghu Machiraju

发明人： Jinho Lee ,  Baback Moghaddam ,  Hanspeter Pfister ,  Raghu Machiraju

IPC分类号： G06K9/00 ,  G06K9/46 ,  G06K9/66 ,  G06K9/36 ,  A61B3/10

CPC分类号： G06K9/4661

摘要： A method generates a three-dimensional, bi-linear, illumination model for arbitrary faces. A large number of images are acquired of many different faces. For each face, multiple images are acquired with varying poses and varying illumination. A three-mode singular value decomposition is applied to the images to determine parameters of the model. The model can be fit to a probe image of an unknown face. Then, the model can be compared with models of a gallery of images of unknown faces to recognize the face in the probe image.

摘要翻译： 一种方法为任意面生成三维，双线性照明模型。 许多不同的脸部获得了大量的图像。 对于每个脸部，以不同的姿势和变化的照明采集多个图像。 对图像应用三模式奇异值分解以确定模型的参数。 该模型可以适应未知脸部的探针图像。 然后，该模型可以与未知脸部图像库的模型进行比较，以识别探针图像中的脸部。

公开(公告)号：US20070156471A1

公开(公告)日：2007-07-05

申请号：US11289343

申请日：2005-11-29

申请人： Baback Moghaddam ,  Yair Weiss ,  Shmuel Avidan

发明人： Baback Moghaddam ,  Yair Weiss ,  Shmuel Avidan

IPC分类号： G06F9/44 ,  G06F17/50 ,  G06Q40/00

CPC分类号： G06K9/6234 ,  G06Q40/00 ,  G06Q40/04

摘要： A method maximizes a candidate solution to a cardinality-constrained combinatorial optimization problem of sparse principal component analysis. An approximate method has as input a covariance matrix A, a candidate solution, and a sparsity parameter k. A variational renormalization for the candidate solution vector x with regards to the eigenvalue structure of the covariance matrix A and the sparsity parameter k is then performed by means of a sub-matrix eigenvalue decomposition of A to obtain a variance maximized k-sparse eigenvector x that is the best possible solution. Another method solves the problem by means of a nested greedy search technique that includes a forward and backward pass. An exact solution to the problem initializes a branch-and-bound search with an output of a greedy solution.

摘要翻译： 一种方法将候选解最大化为稀疏主分量分析的基数约束组合优化问题。 近似方法具有协方差矩阵A，候选解和稀疏参数k作为输入。 然后通过A的子矩阵特征值分解来执行关于协方差矩阵A的特征值结构和稀疏参数k的候选解矢量x的变分重归一化，以获得方差最大化的k-稀疏特征向量x，其中 是最好的解决方案。 另一种方法通过嵌套的贪婪搜索技术来解决问题，该技术包括前进和后退。 问题的确切解决方案使用贪心解决方案的输出初始化分支搜索。

公开(公告)号：US20060182367A1

公开(公告)日：2006-08-17

申请号：US11057828

申请日：2005-02-14

申请人： Baback Moghaddam

发明人： Baback Moghaddam

IPC分类号： G06K9/36

CPC分类号： G06K9/4661 ,  G06K9/00275

摘要： A method determines a direction of a principal light source in an image. An input image I is acquired of an object illuminated by a principal light source from an unknown direction. The input image includes an array of pixels, and each pixel has an intensity I(x, y). An intensity vector {right arrow over (I)} is constructed from the intensities of the input image. An albedo map ρ is defined for the object. An albedo vector {right arrow over (ρ)} is constructed from the albedo map. A shape matrix N is generated for the object. The albedo vector {right arrow over (ρ)} is multiplied by the shape matrix N to obtain a shape-albedo matrix A. Then, a direction s* to the principal light source is estimated from the intensity vector {right arrow over (I)}, the albedo vector {right arrow over (ρ)} and the shape-albedo matrix A according to an optimization s * = arg ⁢   ⁢ min s ⁢  I → - ρ → - As  .

摘要翻译： 方法确定图像中的主光源的方向。 从未知方向获取由主光源照射的物体的输入图像I。 输入图像包括像素阵列，并且每个像素具有强度I（x，y）。 强度向量（右箭头）（I是从输入图像的强度构成的，为对象定义反照率图rho。反照率矢量{（rho）的右箭头}）由反照率图构成，形状矩阵 对于对象生成N，将反照率矢量{right arrow over（rho）}乘以形状矩阵N以获得形状反照矩阵A.然后，从主光源的方向s * 向量{向右箭头（I，反照率矢量{右箭头（rho）}）和根据优化的形状反照矩阵A