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    • 41. 发明授权
    • Effective feature classification in images
    • 图像中的有效特征分类
    • US08315465B1
    • 2012-11-20
    • US12651128
    • 2009-12-31
    • Shumeet BalujaMichele Covell
    • Shumeet BalujaMichele Covell
    • G06K9/62
    • G06K9/6257G06K9/00456
    • In general, the subject matter described in this specification can be embodied in methods, systems, and program products. A plurality of electronic training images that are each classified as displaying substantially pictures is obtained. A plurality of local image features in each of the plurality of electronic training images is identified. A plurality of weak classifiers are recursively applied to the local image features. During each iteration a weak classifier that accurately classifies the local images features is selected. After each selection of a weak classifier features that were misclassified by the selected weak classifier are given greater weight than features that were classified correctly by the selected weak classifier. For each selected weak classifier a hillclimbing algorithm is performed to attempt to improve the weak classifier. A strong classifier that is a weighted combination of the selected weak classifiers on which hillclimbing algorithms have been performed is produced.
    • 通常,本说明书中描述的主题可以体现在方法,系统和程序产品中。 获得被分类为基本上显示图像的多个电子训练图像。 识别多个电子训练图像中的每一个中的多个局部图像特征。 多个弱分类器递归地应用于局部图像特征。 在每次迭代期间,选择精确分类局部图像特征的弱分类器。 在选择的弱分类器被错误分类的弱分类器特征的每个选择之后,被赋予比选择的弱分类器正确分类的特征更大的权重。 对于每个选定的弱分类器,执行山坡计算以尝试改进弱分类器。 一种强分类器,它是已经执行了山地爬坡算法的所选弱分类器的加权组合。
    • 46. 发明授权
    • Canonical correlation analysis of image/control-point location coupling for the automatic location of control points
    • 用于控制点自动定位的图像/控制点位置耦合的规范相关分析
    • US06400828B2
    • 2002-06-04
    • US09781229
    • 2001-02-13
    • Michele CovellMalcolm Slaney
    • Michele CovellMalcolm Slaney
    • 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 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.
    • 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。
    • 48. 发明授权
    • Method and system for estimating jointed-figure configurations
    • 估计接合图配置的方法和系统
    • US6141463A
    • 2000-10-31
    • US984681
    • 1997-12-03
    • Michele CovellSubutai Ahmed
    • Michele CovellSubutai Ahmed
    • G06T7/00G06T7/60G06K9/36
    • G06K9/00369G06T7/004G06T7/606G06T2207/30196
    • To estimate the configuration of a figure in a captured image, a silhouette image of the figure is scanned to create a signed distance image. This image identifies the distance of each pixel in the image to the closest edge of the silhouette, and indicates whether the pixel is inside or outside of the silhouette. Multiple distance images of this type are employed to generate an eigen-points model, which provides an affine mapping from the signed distance images to the limb parameters of an authored skeleton. When a new input image is received, it is first processed to create the signed-distance image, and this image is applied to the eigen-points model to estimate limb parameters, such as the locations of various joints in the figure. From this information, each foreground pixel in the captured image can be assigned to one of the limbs.
    • 为了估计拍摄图像中的图形的配置,扫描图形的剪影图像以创建带符号的距离图像。 该图像识别图像中的每个像素与轮廓的最近边缘的距离,并且指示像素是否在轮廓的内部或外部。 采用这种类型的多距离图像来产生特征点模型,该特征点模型提供从有符号距离图像到创作骨架肢体参数的仿射映射。 当接收到新的输入图像时,首先对其进行处理以创建有符号距离图像,并将该图像应用于特征点模型以估计肢体参数,例如图中各种关节的位置。 根据该信息,拍摄图像中的每个前景像素可以被分配给一个肢体。
    • 49. 发明授权
    • Clustering queries for image search
    • 对图像搜索进行聚类查询
    • US08745059B1
    • 2014-06-03
    • US13482343
    • 2012-05-29
    • Yushi JingMichele CovellStephen Conor Holiday
    • Yushi JingMichele CovellStephen Conor Holiday
    • G06F17/30G06F15/16
    • G06F17/30598G06F17/30256G06F17/3028G06F17/3053
    • Aspects of the subject matter described herein relate to functions used for retrieving image results based on search queries. More specifically, image search queries can be pre-grouped or classified based on visual and semantic similarity. For example, a pairwise image similarity value for a pair of queries can be computed based on one or more of the sum of all of the overlapping the image results, the sum of the image distances between all of the pairs of images in the image results, and the rank of each of the images in the image results. The pairwise image similarity values can then be used to generate image query clusters. Each image query clusters can include a set of queries with high pairwise image similarity values. In some examples, a distance function can be determined for each image query cluster. This data can be used to provide image results.
    • 本文描述的主题的方面涉及用于基于搜索查询来检索图像结果的功能。 更具体地,可以基于视觉和语义相似性对图像搜索查询进行预分组或分类。 例如,可以基于图像结果重叠的全部和之和中的一个或多个来计算一对查询的成对图像相似度值,图像结果中所有图像对之间的图像距离之和 ,以及图像中每个图像的等级。 然后可以使用成对图像相似度值来生成图像查询簇。 每个图像查询群集可以包括具有高成对图像相似度值的一组查询。 在一些示例中,可以为每个图像查询簇确定距离函数。 该数据可用于提供图像结果。