Hierarchical Tree AAM
    21.
    发明申请
    Hierarchical Tree AAM 有权
    分层树AAM

    公开(公告)号:US20120195495A1

    公开(公告)日:2012-08-02

    申请号:US13017891

    申请日:2011-01-31

    IPC分类号: G06K9/62

    摘要: An active appearance model is built by arranging the training images in its training library into a hierarchical tree with the training images at each parent node being divided into two child nodes according to similarities in characteristic features. The number of node levels is such that the number of training images associated with each leaf node is smaller than a predefined maximum. A separate AAM, one per leaf node, is constructed using each leaf node's corresponding training images. In operation, starting at the root node, a test image is compared with each parent node's two child nodes and follows a node-path of model images that most closely matches the test image. The test image is submitted to an AAM selected for being associated with the leaf node at which the test image rests. The selected AAM's output aligned image may be resubmitted to the hierarchical tree if sufficient alignment is not achieved.

    摘要翻译: 通过将其训练库中的训练图像布置到分级树中,根据特征特征的相似性,将每个父节点处的训练图像分为两个子节点,构建主动外观模型。 节点级别的数量使得与每个叶节点相关联的训练图像的数量小于预定义的最大值。 使用每个叶节点的相应训练图像构建单独的AAM,每个叶节点一个。 在操作中,从根节点开始,将测试图像与每个父节点的两个子节点进行比较,并跟随与测试图像最匹配的模型图像的节点路径。 测试图像被提交给被选择用于与测试图像所在的叶节点相关联的AAM。 如果未实现足够的对准,则所选择的AAM的输出对齐图像可以重新提交到分层树。

    Cascaded face model
    22.
    发明授权
    Cascaded face model 有权
    级联面部模型

    公开(公告)号:US08144976B1

    公开(公告)日:2012-03-27

    申请号:US12961350

    申请日:2010-12-06

    IPC分类号: G06K9/00 G06T17/00 G06F19/00

    CPC分类号: G06K9/00281 G06K9/621

    摘要: An Active Appearance Model AAM is trained using expanded library having examples of true outlier images. The AAM creates a first statistical fitting pair (a model image of the class of object and corresponding statistical model fitting) using characteristic features drawn only from the expanded library. All images within the expanded library that the first statistical fitting pair cannot align are collected into a smaller, second library of true outlier cases. A second statistical fitting pair is created using characteristic features drawn only from the second library, and samples not aligned by the second statistical fitting pair are collected into a still smaller, third library. This process is repeated until a desired percentage of all the images within the initial, expanded library have been aligned. In operation, the AAM applies each of its created statistical fitting pairs, in turn, until it has successfully aligned a submitted test image, or until a stop criterion has been reached.

    摘要翻译: 使用具有真实离群图像示例的扩展库训练活动外观模型AAM。 AAM使用仅从扩展库绘制的特征特征创建第一个统计拟合对(对象类和相应的统计模型拟合的模型图像)。 第一个统计拟合对不能对齐的扩展库中的所有图像被收集到较小的第二个真实异常库中。 使用仅从第二库绘制的特征特征创建第二统计拟合对,并且不由第二统计拟合对对齐的样本被收集到更小的第三库中。 重复此过程,直到初始扩展库中的所有图像的所需百分比已对齐。 在操作中,AAM依次应用其每个创建的统计拟合对,直到其成功对齐提交的测试图像,或直到达到停止标准。

    Model-Based Object Image Processing
    23.
    发明申请
    Model-Based Object Image Processing 失效
    基于模型的对象图像处理

    公开(公告)号:US20100013832A1

    公开(公告)日:2010-01-21

    申请号:US12392808

    申请日:2009-02-25

    IPC分类号: G06T17/00

    摘要: Aspects of the present invention include systems and methods for forming generative models, for utilizing those models, or both. In embodiments, an object model fitting system can be developed comprising a 3D active appearance model (AAM) model. The 3D AAM comprises an appearance model comprising a set of subcomponent appearance models that is constrained by a 3D shape model. In embodiments, the 3D AAM may be generated using a balanced set of training images. The object model fitting system may further comprise one or more manifold constraints, one or more weighting factors, or both. Applications of the present invention include, but are not limited to, modeling and/or fitting face images, although the teachings of the present invention can be applied to modeling/fitting other objects.

    摘要翻译: 本发明的方面包括用于形成生成模型的系统和方法,用于利用这些模型或两者。 在实施例中,可以开发包括3D活动外观模型(AAM)模型的对象模型拟合系统。 3D AAM包括由3D形状模型约束的一组子组件外观模型的外观模型。 在实施例中,可以使用平衡的训练图像集来生成3D AAM。 对象模型拟合系统还可以包括一个或多个歧管约束,一个或多个加权因子,或两者。 本发明的应用包括但不限于建模和/或配合面部图像,尽管本发明的教导可以应用于建模/拟合其他对象。

    Confidence based vein image recognition and authentication
    24.
    发明授权
    Confidence based vein image recognition and authentication 有权
    基于置信度的静脉图像识别和认证

    公开(公告)号:US08914313B2

    公开(公告)日:2014-12-16

    申请号:US13552422

    申请日:2012-07-18

    申请人: Jinjun Wang Jing Xiao

    发明人: Jinjun Wang Jing Xiao

    IPC分类号: G06F15/18

    摘要: An indexed hierarchical tree search structure converts each registration sample into an equivalent registration model based on the clustering of its registration item descriptors in the leaf nodes of the hierarchical tree. Query item descriptors from a query sample from someone wanting to be recognized are distributed into the hierarchical tree. A query model is defined based on the clustering of query item descriptors at the leaf nodes, and registration and verification are made based on comparison of the query model and the registration models.

    摘要翻译: 索引分层树搜索结构基于分层树叶节点中其注册项描述符的聚类将每个注册样本转换为等效的注册模型。 从想要被识别的人的查询样本中查询项目描述符被分发到分层树中。 基于叶节点上查询项描述符的聚类定义查询模型,并根据查询模型与注册模型的比较进行注册和验证。

    High-resolution magnetocardiogram restoration for cardiac electric current localization
    25.
    发明授权
    High-resolution magnetocardiogram restoration for cardiac electric current localization 有权
    心电流定位的高分辨率心电图恢复

    公开(公告)号:US08688192B2

    公开(公告)日:2014-04-01

    申请号:US13017869

    申请日:2011-01-31

    申请人: Chenyu Wu Jing Xiao

    发明人: Chenyu Wu Jing Xiao

    IPC分类号: A61B5/04

    CPC分类号: A61B5/04007 A61B2562/046

    摘要: Magnetocardiogram (MCG) provides temporal and spatial measurements of cardiac electric activities, which permits current localization. An MCG device usually consists of a small number of magnetic sensors in a planar array. Each sensor provides a highly low-resolution 2D MCG map. Such a low-res map is insufficient for cardiac electric current localization. To create a high resolution MCG image from the sparse measurements, an algorithm based on model learning is used. The model is constructed using a large number of randomly generated high resolution MCG images based on the Biot-Savart Law. By fitting the model with the sparse measurements, high resolution MCG image are created. Next, the 2D position of the electric current is localized by finding the peak in the tangential components of the high resolution MCG images. Finally, the 2D current localization is refined by a non-linear optimization algorithm, which simultaneously recovers the depth of the electric current from the sensor and its magnitude and orientation.

    摘要翻译: 心电图(MCG)提供心脏电活动的时间和空间测量,这允许当前的定位。 MCG器件通常由平面阵列中的少量磁传感器组成。 每个传感器提供高度低分辨率的2D MCG图。 这样的低分辨率图不足以用于心电流定位。 为了从稀疏测量中创建高分辨率MCG图像,使用基于模型学习的算法。 该模型使用基于Biot-Savart定律的大量随机生成的高分辨率MCG图像来构建。 通过使用稀疏测量拟合模型,创建高分辨率MCG图像。 接下来,通过找到高分辨率MCG图像的切向分量中的峰值来定位电流的2D位置。 最后,通过非线性优化算法对2D电流定位进行了改进,该算法同时恢复了传感器电流的深度及其幅度和方向。

    Contextual boost for object detection
    26.
    发明授权
    Contextual boost for object detection 有权
    对象检测的上下文提升

    公开(公告)号:US08538081B2

    公开(公告)日:2013-09-17

    申请号:US13371847

    申请日:2012-02-13

    IPC分类号: G06K9/00

    摘要: Aspects of the present invention includes systems and methods for generating detection models that consider contextual information of an image patch and for using detection models that consider contextual information. In embodiments, a multi-scale image context descriptor is generated to represent the contextual cues in multiple parameters, such as spatial, scaling, and color spaces. In embodiments, a classification context is defined using the contextual features and is used in a contextual boost classification scheme. In embodiments, the contextual boost propagates contextual cues to larger coverage through iterations to improve the detection accuracy.

    摘要翻译: 本发明的方面包括用于产生考虑图像补丁的上下文信息以及使用考虑上下文信息的检测模型的检测模型的系统和方法。 在实施例中,生成多尺度图像上下文描述符以表示诸如空间,缩放和颜色空间的多个参数中的上下文提示。 在实施例中,使用上下文特征来定义分类上下文,并且在上下文增强分类方案中使用。 在实施例中,上下文提升通过迭代将上下文提示传播到更大的覆盖范围,以提高检测精度。

    Substructure and Boundary Modeling for Continuous Action Recognition
    27.
    发明申请
    Substructure and Boundary Modeling for Continuous Action Recognition 有权
    连续动作识别的子结构和边界建模

    公开(公告)号:US20130132316A1

    公开(公告)日:2013-05-23

    申请号:US13491108

    申请日:2012-06-07

    IPC分类号: G06N5/02

    CPC分类号: G06N99/005

    摘要: Embodiments of the present invention include systems and methods for improved state space modeling (SSM) comprising two added layers to model the substructure transition dynamics and action duration distribution. In embodiments, the first layer represents a substructure transition model that encodes the sparse and global temporal transition probability. In embodiments, the second layer models the action boundary characteristics by injecting discriminative information into a logistic duration model such that transition boundaries between successive actions can be located more accurately; thus, the second layer exploits discriminative information to discover action boundaries adaptively.

    摘要翻译: 本发明的实施例包括用于改进状态空间建模(SSM)的系统和方法,所述状态空间建模(SSM)包括两个附加的层,以模拟子结构转变动力学和动作持续时间分布。 在实施例中,第一层表示编码稀疏和全局时间转移概率的子结构转换模型。 在实施例中,第二层通过将识别信息注入逻辑持续时间模型来建模动作边界特征,使得可以更准确地定位连续动作之间的转移边界; 因此,第二层利用辨别信息自动发现行动界限。

    Ray image modeling for fast catadioptric light field rendering
    28.
    发明授权
    Ray image modeling for fast catadioptric light field rendering 有权
    用于快速反射折射光场渲染的射线图像建模

    公开(公告)号:US08432435B2

    公开(公告)日:2013-04-30

    申请号:US13207224

    申请日:2011-08-10

    IPC分类号: H04N7/12

    摘要: A catadioptric camera creates image light fields from a 3D scene by creating ray images defined as 2D arrays of ray-structure picture-elements (ray-xels). Each ray-xel captures light intensity, mirror-reflection location, and mirror-incident light ray direction. A 3D image is then rendered from the ray images by combining the corresponding ray-xels.

    摘要翻译: 反射折射照相机通过创建被定义为射线结构图像元素(ray-xels)的2D阵列的射线图像,从3D场景创建图像光场。 每个ray-xel捕获光强度,镜面反射位置和镜像入射光线方向。 然后通过组合相应的射线 - xels从射线图像渲染3D图像。

    Method for constraint optimization under box constraints
    29.
    发明授权
    Method for constraint optimization under box constraints 有权
    方框约束下的约束优化方法

    公开(公告)号:US08407171B2

    公开(公告)日:2013-03-26

    申请号:US12853886

    申请日:2010-08-10

    IPC分类号: G06F17/00

    CPC分类号: G06F15/18 G06F17/16

    摘要: Similarities between simplex projection with upper bounds and L1 projection are explored. Criteria for a-priori determination of sequence in which various constraints become active are derived, and this sequence is used to develop efficient algorithms for projecting a vector onto the L1-ball while observing box constraints. Three projection methods are presented. The first projection method performs exact projection in O(n2) worst case complexity, where n is the space dimension. Using a novel criteria for ordering constraints, the second projection method has a worst case complexity of O(n log n). The third projection method is a worst case linear time algorithm having O(n) complexity. The upper bounds defined for the projected entries guide the L1-ball projection to more meaningful predictions.

    摘要翻译: 探讨了单面投影与上界和L1投影之间的相似性。 导出先验确定各种约束变为有效的序列的标准,并且该序列用于开发用于在观察盒约束的情况下将向量投影到L1球上的有效算法。 提出了三种投影方法。 第一种投影方法在O(n2)最差情况复杂度中执行精确投影,其中n是空间维数。 使用新颖的排序约束条件,第二种投影方法具有O(n log n)的最差情况复杂度。 第三种投影方法是具有O(n)复杂度的最差情况线性时间算法。 为投影条目定义的上限将引导L1球投影更有意义的预测。