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    • 98. 发明授权
    • Principle component analysis of images for the automatic location of control points
    • 控制点自动定位图像的原理分量分析
    • US06188776B1
    • 2001-02-13
    • US08651108
    • 1996-05-21
    • Michele CovellChristoph Bregler
    • Michele CovellChristoph Bregler
    • G06K900
    • G06K9/00281G06K9/4609G06K9/48G06K9/6211G06K2009/487
    • The identification of hidden data, such as feature-based control points in an image, from a set of observable data, such as the image, is achieved through a two-stage approach. The first stage involves a learning process, in which a number of sample data sets, e.g. images, are analyzed to identify the correspondence between observable data, such as visual aspects of the image, and the desired hidden data, such as the control points. Two models are created. A feature appearance-only model is created from aligned examples of the feature in the observed data. In addition, each labeled data set is processed to generate a coupled model of the aligned observed data and the associated hidden data. In the image processing embodiment, these two models might be affine manifold models of an object's appearance and of the coupling between that appearance and a set of locations on the object's surface. In the second stage of the process, the modeled feature is located in an unmarked, unaligned data set, using the feature appearance-only model. This location is used as an alignment point and the coupled model is then applied to the aligned data, giving an estimate of the hidden data values for that data set. In the image processing example, the object's appearance model is compared to different image locations. The matching locations are then used as alignment points for estimating the locations on the object's surface from the appearance in that aligned image and form the coupled model.
    • 通过两阶段方法,可以从一组可观察数据(如图像)中识别隐藏数据,如图像中基于特征的控制点。 第一阶段涉及学习过程,其中多个样本数据集,例如, 分析图像以识别诸如图像的视觉方面的可观察数据与期望的隐藏数据(例如控制点)之间的对应关系。 创建了两个模型。 仅从观察数据中的特征的对齐示例创建仅出现特征的模型。 此外,处理每个标记的数据集以生成对准的观察数据和相关联的隐藏数据的耦合模型。 在图像处理实施例中,这两个模型可以是对象的外观和该外观与物体表面上的一组位置之间的耦合的仿射歧管模型。 在该过程的第二阶段,建模特征位于未标记的未对齐数据集中,使用仅特征外观模型。 该位置用作对齐点,然后将耦合模型应用于对齐的数据,给出该数据集的隐藏数据值的估计。 在图像处理示例中,将对象的外观模型与不同的图像位置进行比较。 然后将匹配位置用作对准点,以从该对准图像中的外观估计对象表面上的位置并形成耦合模型。