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
    • 9. 发明授权
    • Method for comparing features extracted from images of fingerprints
    • 用于比较从指纹图像中提取的特征的方法
    • US07986820B2
    • 2011-07-26
    • US10087409
    • 2001-10-19
    • Baback Moghaddam
    • Baback Moghaddam
    • G06K9/00G06K9/46G06K9/74G08K9/40
    • G06K9/00087
    • 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.
    • 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。
    • 10. 发明申请
    • Spectral method for sparse principal component analysis
    • 稀疏主成分分析的光谱法
    • US20070156471A1
    • 2007-07-05
    • US11289343
    • 2005-11-29
    • Baback MoghaddamYair WeissShmuel Avidan
    • Baback MoghaddamYair WeissShmuel Avidan
    • G06F9/44G06F17/50G06Q40/00
    • G06K9/6234G06Q40/00G06Q40/04
    • 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.
    • 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。