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
    • 5. 发明申请
    • Method and apparatus for propagating high resolution detail between multimodal data sets
    • 在多模态数据集之间传播高分辨率细节的方法和装置
    • US20070104393A1
    • 2007-05-10
    • US11271367
    • 2005-11-10
    • Dennis Strelow
    • Dennis Strelow
    • G06K9/32G06K9/00
    • G06T3/4007
    • The dataset describing an entity in a first modality and of a first, high resolution is used to enhance the resolution of a dataset describing the same entity in a second modality of a lower resolution. The two data sets of different modalities are spatially registered to each other. From this information, a joint histogram of the values in the two datasets is computed to provide a raw analysis of how the intensities in the first dataset correspond to intensities in the second dataset. This is converted into a joint probability of possible intensities for the missing pixels in the low resolution dataset as a function of the intensities of the corresponding pixels in the high-resolution dataset to provide a very rough estimate of the intensities of the missing pixels in the low resolution dataset. Then, an objective function is defined over the set of possible new values that gives preference to datasets consistent with (1) the joint probability distributions, (2) the existing values in the low resolution dataset, and (3) smoothness throughout the data set. Finally, an annealing or similar iterative method is used to minimize the objective function and find an optimal solution over the entire dataset.
    • 描述第一模态和第一高分辨率中的实体的数据集用于增强以较低分辨率的第二模态描述相同实体的数据集的分辨率。 不同模态的两个数据集在空间上相互对照。 从该信息中,计算两个数据集中的值的联合直方图,以提供关于第一数据集中的强度对应于第二数据集中的强度的原始分析。 这被转换为低分辨率数据集中的丢失像素的可能强度的联合概率,作为高分辨率数据集中对应像素的强度的函数,以提供对于在高分辨率数据集中的丢失像素的强度的非常粗略的估计 低分辨率数据集。 然后,对一组可能的新值定义目标函数,该值对于与(1)联合概率分布一致的数据集优先考虑,(2)低分辨率数据集中的现有值,以及(3)整个数据集中的平滑度 。 最后,使用退火或类似的迭代方法来最小化目标函数,并在整个数据集上找到最优解。
    • 6. 发明授权
    • General and nested Wiberg minimization
    • 一般和嵌套Wiberg最小化
    • US08959128B1
    • 2015-02-17
    • US13297709
    • 2011-11-16
    • Dennis StrelowJay Yagnik
    • Dennis StrelowJay Yagnik
    • G06F7/38
    • G06F17/16G06F17/11G06T7/246G06T7/70G06T7/73G06T2207/10016G06T2207/30244
    • Wiberg minimization operates on a system with two sets of variables described by a linear function and in which some data or observations are missing. The disclosure generalizes Wiberg minimization, solving for a function that is nonlinear in both sets of variables, U and V, iteratively. In one embodiment, defining a first function ƒ(U, V) that may be defined that may be nonlinear in both a first set of variables U and a second set of variables V. A first function ƒ(U, V) may be transformed into ƒ(U, V(U)). First assumed values of the first set of variables U may be assigned. The second set of variables V may be iteratively estimated based upon the transformed first function ƒ(U, V(U)) and the assumed values of the first set of variables U such that ƒ(U, V(U)) may be minimized with respect to V. New estimates of the first set of variables U may be iteratively computed.
    • Wiberg最小化操作在具有由线性函数描述的两组变量的系统中,其中缺少一些数据或观察值。 本公开概括了Wiberg最小化,迭代地求解了两组变量U和V中的非线性函数。 在一个实施例中,定义可以被定义为在第一变量集U和第二组变量V中都可以是非线性的第一函数ƒ(U,V)。可以变换第一函数ƒ(U,V) 进入ƒ(U,V(U))。 可以分配第一组变量U的第一假设值。 可以基于变换的第一函数ƒ(U,V(U))和第一变量集合U的假定值来迭代地估计第二组变量V,使得ƒ(U,V(U))可以被最小化 可以迭代地计算第一组变量U的新估计。