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    • 4. 发明授权
    • Picture/graphics classification system and method
    • 图片/图形分类系统和方法
    • US06983068B2
    • 2006-01-03
    • US09965922
    • 2001-09-28
    • Salil PrabhakarHui ChengZhigang FanJohn C. HandleyYing-wei Lin
    • Salil PrabhakarHui ChengZhigang FanJohn C. HandleyYing-wei Lin
    • G06K9/00
    • G06K9/00456
    • A method and system for image processing, in conjunction with classification of images between natural pictures and synthetic graphics, using SGLD texture (e.g., variance, bias, skewness, and fitness), color discreteness (e.g., R—L, R—U, and R—V normalized histograms), or edge features (e.g., pixels per detected edge, horizontal edges, and vertical edges) is provided. In another embodiment, a picture/graphics classifier using combinations of SGLD texture, color discreteness, and edge features is provided. In still another embodiment, a “soft” image classifier using combinations of two (2) or more SGLD texture, color discreteness, and edge features is provided. The “soft” classifier uses image features to classify areas of an input image in picture, graphics, or fuzzy classes.
    • 一种用于图像处理的方法和系统,结合使用SGLD纹理(例如,方差,偏差,偏度和适应度)的自然图像和合成图像之间的图像分类,颜色离散性(例如,R SUB 提供了一个或多个边缘特征(例如每个检测到的边缘,水平边缘和垂直边缘的像素)。 在另一个实施例中,提供了使用SGLD纹理,颜色离散性和边缘特征的组合的图片/图形分类器。 在另一个实施例中,提供了使用两(2)或更多SGLD纹理,颜色离散性和边缘特征的组合的“软”图像分类器。 “软”分类器使用图像特征来对图像,图形或模糊类中的输入图像的区域进行分类。
    • 8. 发明授权
    • Exemplar-based heterogeneous compositional method for object classification
    • 用于对象分类的基于示例的异构组合方法
    • US08233704B2
    • 2012-07-31
    • US12136138
    • 2008-06-10
    • Feng HanHui ChengJiangjian XiaoHarpreet Singh Sawhney
    • Feng HanHui ChengJiangjian XiaoHarpreet Singh Sawhney
    • G06K9/62G06E1/00G06E3/00G06F15/18G06G7/00
    • G06K9/3241G06K9/6256G06K9/6292
    • A method for automatically generating a strong classifier for determining whether at least one object is detected in at least one image is disclosed, comprising the steps of: (a) receiving a data set of training images having positive images; (b) randomly selecting a subset of positive images from the training images to create a set of candidate exemplars, wherein said positive images include at least one object of the same type as the object to be detected; (c) training a weak classifier based on at least one of the candidate exemplars, said training being based on at least one comparison of a plurality of heterogeneous compositional features located in the at least one image and corresponding heterogeneous compositional features in the one of set of candidate exemplars; (d) repeating steps (c) for each of the remaining candidate exemplars; and (e) combining the individual classifiers into a strong classifier, wherein the strong classifier is configured to determine the presence or absence in an image of the object to be detected.
    • 公开了一种用于自动生成强分类器以确定在至少一个图像中是否检测到至少一个对象的方法,包括以下步骤:(a)接收具有正图像的训练图像的数据集; (b)从所述训练图像中随机选择正图像的子集以创建一组候选样本,其中所述正图像包括与所述待检测对象相同类型的至少一个对象; (c)基于所述候选样本中的至少一个来训练弱分类器,所述训练基于位于所述至少一个图像中的多个异质成分特征和所述一个图像中的一个中的对应的异质组成特征的至少一个比较 候选人样本 (d)为每个其余的候选样本重复步骤(c); 以及(e)将各个分类器组合成强分类器,其中强分类器被配置为确定待检测对象的图像中的存在或不存在。
    • 9. 发明申请
    • METHOD FOR BUILDING AND EXTRACTING ENTITY NETWORKS FROM VIDEO
    • 从视频建立和提取实体网络的方法
    • US20090153661A1
    • 2009-06-18
    • US12271173
    • 2008-11-14
    • Hui ChengJiangjian XiaoHarpreet Sawhney
    • Hui ChengJiangjian XiaoHarpreet Sawhney
    • H04N7/18
    • G06T7/20G06K9/00771G06K2009/3291
    • A computer implemented method for deriving an attribute entity network (AEN) from video data is disclosed, comprising the steps of: extracting at least two entities from the video data; tracking the trajectories of the at least two entities to form at least two tracks; deriving at least one association between at least two entities by detecting at least one event involving the at least two entities, said detecting of at least one event being based on detecting at least one spatio-temporal motion correlation between the at least two entities; and constructing the AEN by creating a graph wherein the at least two objects form at least two nodes and the at least one association forms a link between the at least two nodes.
    • 公开了一种用于从视频数据中导出属性实体网络(AEN)的计算机实现方法,包括以下步骤:从视频数据中提取至少两个实体; 跟踪所述至少两个实体的轨迹以形成至少两个轨道; 通过检测涉及所述至少两个实体的至少一个事件来导出至少两个实体之间的至少一个关联,所述至少一个事件的检测是基于检测所述至少两个实体之间的至少一个时空运动相关性; 以及通过创建图形来构建所述AEN,其中所述至少两个对象形成至少两个节点,并且所述至少一个关联形成所述至少两个节点之间的链接。
    • 10. 发明申请
    • EXEMPLAR-BASED HETEROGENEOUS COMPOSITIONAL METHOD FOR OBJECT CLASSIFICATION
    • 用于对象分类的基于EXEMPLAR的异构组合方法
    • US20080310737A1
    • 2008-12-18
    • US12136138
    • 2008-06-10
    • Feng HanHui ChengJiangjian XiaoHarpreet Singh Sawhney
    • Feng HanHui ChengJiangjian XiaoHarpreet Singh Sawhney
    • G06K9/62
    • G06K9/3241G06K9/6256G06K9/6292
    • A method for automatically generating a strong classifier for determining whether at least one object is detected in at least one image is disclosed, comprising the steps of: (a) receiving a data set of training images having positive images; (b) randomly selecting a subset of positive images from the training images to create a set of candidate exemplars, wherein said positive images include at least one object of the same type as the object to be detected; (c) training a weak classifier based on at least one of the candidate exemplars, said training being based on at least one comparison of a plurality of heterogeneous compositional features located in the at least one image and corresponding heterogeneous compositional features in the one of set of candidate exemplars; (d) repeating steps (c) for each of the remaining candidate exemplars; and (e) combining the individual classifiers into a strong classifier, wherein the strong classifier is configured to determine the presence or absence in an image of the object to be detected.
    • 公开了一种用于自动生成强分类器以确定在至少一个图像中是否检测到至少一个对象的方法,包括以下步骤:(a)接收具有正图像的训练图像的数据集; (b)从所述训练图像中随机选择正图像的子集以创建一组候选样本,其中所述正图像包括与所述待检测对象相同类型的至少一个对象; (c)基于所述候选样本中的至少一个训练弱分类器,所述训练基于位于所述至少一个图像中的多个异质成分特征和所述一个图像中的一个中的对应的异质组成特征的至少一个比较 候选人样本 (d)为每个其余的候选样本重复步骤(c); 以及(e)将各个分类器组合成强分类器,其中强分类器被配置为确定待检测对象的图像中的存在或不存在。