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    System and method for learning relative distance in a shape space using image based features
    1.
    发明授权
    System and method for learning relative distance in a shape space using image based features 有权
    使用基于图像的特征来学习形状空间中的相对距离的系统和方法

    公开(公告)号：US07603000B2

    公开(公告)日：2009-10-13

    申请号：US11464851

    申请日：2006-08-16

    申请人： Yefeng Zheng ,  Xiang Zhou ,  Shaohua Kevin Zhou ,  Bogdan Georgescu ,  Dorin Comaniciu

    发明人： Yefeng Zheng ,  Xiang Zhou ,  Shaohua Kevin Zhou ,  Bogdan Georgescu ,  Dorin Comaniciu

    IPC分类号： G06K9/60

    CPC分类号： G06K9/6255 ,  G06K9/6206 ,  G06K9/6256

    摘要： A system and method for identifying a shape of an anatomical structure in an input image is disclosed. An input image is received and warped using a set of warping templates resulting in a set of warped images. An integral image is calculated for each warped image. Selected features are extracted based on the integral image. A boosted feature score is calculated for the combined selected features for each warped image. The warped images are ranked based on the boosted feature scores. A predetermined number of warped images are selected that have the largest feature scores. Each selected warped image is associated with its corresponding warping template. The corresponding warping templates are associated with stored shape models. The shape of the input image is identified based on the weighted average of the shapes models.

    摘要翻译： 公开了一种用于识别输入图像中的解剖结构的形状的系统和方法。 使用一组翘曲模板接收和扭曲输入图像，产生一组翘曲图像。 为每个弯曲图像计算整体图像。 基于积分图像提取所选特征。 对于每个弯曲图像的组合选定特征，计算提升的特征分数。 翘曲的图像根据提升的特征得分进行排名。 选择具有最大特征分数的预定数量的翘曲图像。 每个选择的变形图像与其相应的翘曲模板相关联。 相应的变形模板与存储的形状模型相关联。 基于形状模型的加权平均值来识别输入图像的形状。

    SYSTEM AND METHOD FOR LEARNING RELATIVE DISTANCE IN A SHAPE SPACE USING IMAGE BASED FEATURES
    3.
    发明申请
    SYSTEM AND METHOD FOR LEARNING RELATIVE DISTANCE IN A SHAPE SPACE USING IMAGE BASED FEATURES 有权
    使用基于图像的特征在形状空间中学习相对距离的系统和方法

    公开(公告)号：US20070046696A1

    公开(公告)日：2007-03-01

    申请号：US11464851

    申请日：2006-08-16

    申请人： Yefeng Zheng ,  Xiang Zhou ,  Shaohua Zhou ,  Bogdan Georgescu ,  Dorin Comaniciu

    发明人： Yefeng Zheng ,  Xiang Zhou ,  Shaohua Zhou ,  Bogdan Georgescu ,  Dorin Comaniciu

    IPC分类号： G09G5/00

    CPC分类号： G06K9/6255 ,  G06K9/6206 ,  G06K9/6256

    摘要： A system and method for identifying a shape of an anatomical structure in an input image is disclosed. An input image is received and warped using a set of warping templates resulting in a set of warped images. An integral image is calculated for each warped image. Selected features are extracted based on the integral image. A boosted feature score is calculated for the combined selected features for each warped image. The warped images are ranked based on the boosted feature scores. A predetermined number of warped images are selected that have the largest feature scores. Each selected warped image is associated with its corresponding warping template. The corresponding warping templates are associated with stored shape models. The shape of the input image is identified based on the weighted average of the shapes models.

    摘要翻译： 公开了一种用于识别输入图像中的解剖结构的形状的系统和方法。 使用一组翘曲模板接收和扭曲输入图像，产生一组翘曲图像。 为每个弯曲图像计算整体图像。 基于积分图像提取所选特征。 对于每个弯曲图像的组合选定特征，计算提升的特征分数。 翘曲的图像根据提升的特征得分进行排名。 选择具有最大特征分数的预定数量的翘曲图像。 每个选择的变形图像与其相应的翘曲模板相关联。 相应的变形模板与存储的形状模型相关联。 基于形状模型的加权平均值来识别输入图像的形状。

    Scalable Semantic Image Search
    4.
    发明申请
    Scalable Semantic Image Search 审中-公开
    可扩展语义图像搜索

    公开(公告)号：US20080027917A1

    公开(公告)日：2008-01-31

    申请号：US11767920

    申请日：2007-06-25

    申请人： Saikat Mukherjee ,  Shaohua Kevin Zhou ,  Xiang Zhou ,  Martin Huber ,  Jorg Freund ,  Volker Tresp ,  Sonja Zillner ,  Alok Gupta ,  Dorin Comaniciu

    发明人： Saikat Mukherjee ,  Shaohua Kevin Zhou ,  Xiang Zhou ,  Martin Huber ,  Jorg Freund ,  Volker Tresp ,  Sonja Zillner ,  Alok Gupta ,  Dorin Comaniciu

    IPC分类号： G06F17/30

    CPC分类号： G06F16/583 ,  G06N5/022

    摘要： A computer-implemented system for searching a plurality of images for an image of interest including a database of semantic image representations corresponding to the plurality of images, wherein the semantic image representations link a semantic model of clinical properties, a syntactic model of high level image properties and an image vocabulary of low level image properties, a set of queries associated with the semantic image representations, and a semantic search engine, embodied as computer readable code executed by a processor, for receiving a search query, selecting at least one of the set of queries based on the search query, and searching the plurality of images for the image of interest by comparing the plurality of images against the semantic image representations associated with a selected query.

    摘要翻译： 一种用于搜索感兴趣的图像的多个图像的计算机实现的系统，包括与多个图像相对应的语义图像表示的数据库，其中所述语义图像表示链接临床属性的语义模型，高级图像的句法模型 低级图像属性的属性和图像词汇表，与语义图像表示相关联的一组查询，以及体现为由处理器执行的计算机可读代码的语义搜索引擎，用于接收搜索查询，选择至少一个 基于搜索查询的查询集合，以及通过将多个图像与与所选择的查询相关联的语义图像表示进行比较来搜索多个图像中的感兴趣图像。

    Method for performing image based regression using boosting
    5.
    发明申请
    Method for performing image based regression using boosting 有权
    使用升压进行基于图像的回归的方法

    公开(公告)号：US20070071313A1

    公开(公告)日：2007-03-29

    申请号：US11372782

    申请日：2006-03-10

    申请人： Shaohua Zhou ,  Bogdan Georgescu ,  Xiang Zhou ,  Dorin Comaniciu

    发明人： Shaohua Zhou ,  Bogdan Georgescu ,  Xiang Zhou ,  Dorin Comaniciu

    IPC分类号： G06K9/62

    CPC分类号： G06K9/6256 ,  G06K2009/00322 ,  G06K2209/05

    摘要： A method for performing image based regression using boosting to infer an entity that is associated with an image of an object is disclosed. A regression function for a plurality of images is learned in which for each image the associated entity is known. The learned regression function is used to predict an entity associated with an image in which the entity is not known.

    摘要翻译： 公开了一种使用增强来执行基于图像的回归的方法来推断与对象的图像相关联的实体。 学习用于多个图像的回归函数，其中对于每个图像，相关联的实体是已知的。 学习的回归函数用于预测与实体不知道的图像相关联的实体。

    Method and system for multi-modal component-based tracking of an object using robust information fusion
    6.
    发明申请
    Method and system for multi-modal component-based tracking of an object using robust information fusion 失效
    使用鲁棒信息融合的多模态组件跟踪对象的方法和系统

    公开(公告)号：US20050185826A1

    公开(公告)日：2005-08-25

    申请号：US11058784

    申请日：2005-02-16

    申请人： Bogdan Georgescu ,  Xiang Zhou ,  Dorin Comaniciu ,  R. Rao

    发明人： Bogdan Georgescu ,  Xiang Zhou ,  Dorin Comaniciu ,  R. Rao

    IPC分类号： G06K9/64 ,  G06T7/20 ,  G06K9/00

    CPC分类号： G06K9/3216 ,  G06K9/6206 ,  G06T7/215 ,  G06T7/269

    摘要： A system and method for tracking an object is disclosed. A video sequence including a plurality of image frames are received. A sample based representation of object appearance distribution is maintained. An object is divided into one or more components. For each component, its location and uncertainty with respect to the sample based representation are estimated. Variable-Bandwidth Density Based Fusion (VBDF) is applied to each component to determine a most dominant motion. The motion estimate is used to determine the track of the object.

    摘要翻译： 公开了一种跟踪对象的系统和方法。 接收包括多个图像帧的视频序列。 维护对象外观分布的基于样本的表示。 一个对象被分成一个或多个组件。 对于每个组件，估计其相对于基于样本的表示的位置和不确定性。 基于可变带宽密度的融合（VBDF）应用于每个组件以确定最主要的运动。 运动估计用于确定对象的轨迹。

    Method for database guided simultaneous multi slice object detection in three dimensional volumetric data
    8.
    发明申请
    Method for database guided simultaneous multi slice object detection in three dimensional volumetric data 有权
    三维体积数据中数据库引导同时多切片物体检测方法

    公开(公告)号：US20070036414A1

    公开(公告)日：2007-02-15

    申请号：US11502709

    申请日：2006-08-10

    申请人： Bogdan Georgescu ,  Xiang Zhou ,  Wei Hong ,  Dorin Comaniciu ,  Sriram Krishnan

    发明人： Bogdan Georgescu ,  Xiang Zhou ,  Wei Hong ,  Dorin Comaniciu ,  Sriram Krishnan

    IPC分类号： G06K9/00 ,  G06K9/62

    CPC分类号： G06T7/149 ,  G06T7/12 ,  G06T2207/10136 ,  G06T2207/20081 ,  G06T2207/30048

    摘要： The present invention is directed to a method for automatic detection and segmentation of a target anatomical structure in received three dimensional (3D) volumetric medical images using a database of a set of volumetric images with expertly delineated anatomical structures. A 3D anatomical structure detection and segmentation module is trained offline by learning anatomical structure appearance using the set of expertly delineated anatomical structures. A received volumetric image for the anatomical structure of interest is searched online using the offline learned 3D anatomical structure detection and segmentation module.

    摘要翻译： 本发明涉及一种使用具有专门描绘的解剖结构的一组体积图像的数据库来自动检测和分割接收的三维（3D）体积医学图像中的目标解剖结构的方法。 3D解剖结构检测和分割模块通过使用专门描绘的解剖结构集合学习解剖结构外观来进行离线训练。 使用离线学习的3D解剖结构检测和分割模块在线搜索用于感兴趣解剖结构的接收体积图像。