-
1.发明授权公开(公告)号：US08190549B2
公开(公告)日：2012-05-29
申请号：US12276128
申请日：2008-11-21
CPC分类号： G06K9/32 , G06K9/6214 , G06K9/6273
摘要： An online sparse matrix Gaussian process (OSMGP) uses online updates to provide an accurate and efficient regression for applications such as pose estimation and object tracking. A regression calculation module calculates a regression on a sequence of input images to generate output predictions based on a learned regression model. The regression model is efficiently updated by representing a covariance matrix of the regression model using a sparse matrix factor (e.g., a Cholesky factor). The sparse matrix factor is maintained and updated in real-time based on the output predictions. Hyperparameter optimization, variable reordering, and matrix downdating techniques can also be applied to further improve the accuracy and/or efficiency of the regression process.
摘要翻译： 在线稀疏矩阵高斯过程（OSMGP）使用在线更新来为姿态估计和对象跟踪等应用程序提供准确有效的回归。 回归计算模块根据学习的回归模型计算输入图像序列的回归以产生输出预测。 通过使用稀疏矩阵因子（例如，Cholesky因子）表示回归模型的协方差矩阵来有效地更新回归模型。 基于输出预测，稀疏矩阵因子实时维护和更新。 超参数优化，可变重排序和矩阵减法技术也可以应用于进一步提高回归过程的准确性和/或效率。
-
2.发明申请公开(公告)号：US20090164405A1
公开(公告)日：2009-06-25
申请号：US12276128
申请日：2008-11-21
IPC分类号： G06N5/02
CPC分类号： G06K9/32 , G06K9/6214 , G06K9/6273
摘要： An online sparse matrix Gaussian process (OSMGP) uses online updates to provide an accurate and efficient regression for applications such as pose estimation and object tracking. A regression calculation module calculates a regression on a sequence of input images to generate output predictions based on a learned regression model. The regression model is efficiently updated by representing a covariance matrix of the regression model using a sparse matrix factor (e.g., a Cholesky factor). The sparse matrix factor is maintained and updated in real-time based on the output predictions. Hyperparameter optimization, variable reordering, and matrix downdating techniques can also be applied to further improve the accuracy and/or efficiency of the regression process.
摘要翻译： 在线稀疏矩阵高斯过程（OSMGP）使用在线更新来为姿态估计和对象跟踪等应用程序提供准确有效的回归。 回归计算模块根据学习的回归模型计算输入图像序列的回归以产生输出预测。 通过使用稀疏矩阵因子（例如，Cholesky因子）表示回归模型的协方差矩阵来有效地更新回归模型。 基于输出预测，稀疏矩阵因子实时维护和更新。 超参数优化，可变重排序和矩阵减法技术也可以应用于进一步提高回归过程的准确性和/或效率。
-
公开(公告)号：US20090041310A1
公开(公告)日：2009-02-12
申请号：US10703288
申请日：2003-11-06
申请人： Ming-Hsuan Yang , Jeffrey Ho , Kuang-Chih Lee
发明人： Ming-Hsuan Yang , Jeffrey Ho , Kuang-Chih Lee
CPC分类号： G06K9/00335 , G06K9/00275 , G06K9/00288 , G06K9/3208 , G06K9/6232
摘要： The present invention meets these needs by providing temporal coherency to recognition systems. One embodiment of the present invention comprises a manifold recognition module to use a sequence of images for recognition. A manifold training module receives a plurality of training image sequences (e.g. from a video camera), each training image sequence including an individual in a plurality of poses, and establishes relationships between the images of a training image sequence. A probabilistic identity module receives a sequence of recognition images including a target individual for recognition, and identifies the target individual based on the relationship of training images corresponding to the recognition images. An occlusion module masks occluded portions of an individual's face to prevent distorted identifications.
摘要翻译： 本发明通过向识别系统提供时间一致性来满足这些需求。 本发明的一个实施例包括使用一系列图像进行识别的歧管识别模块。 歧管训练模块接收多个训练图像序列（例如，从摄像机），每个训练图像序列包括多个姿势中的个体，并且建立训练图像序列的图像之间的关系。 概率识别模块接收包括用于识别的目标个体的识别图像序列，并且基于与识别图像相对应的训练图像的关系来识别目标个体。 闭塞模块遮挡个人脸部的遮挡部分以防止变形的识别。
-
4.发明授权公开(公告)号：US07463754B2
公开(公告)日：2008-12-09
申请号：US10989966
申请日：2004-11-15
申请人： Ming-Hsuan Yang , Jongwoo Lim , David Ross , Ruei-Sung Lin
发明人： Ming-Hsuan Yang , Jongwoo Lim , David Ross , Ruei-Sung Lin
IPC分类号： G06K9/00
CPC分类号： G06K9/621 , G06K9/3241 , G06T7/207
摘要： A system and a method are disclosed for adaptive probabilistic tracking of an object within a motion video. The method utilizes a time-varying Eigenbasis and dynamic, observation and inference models. The Eigenbasis serves as a model of the target object. The dynamic model represents the motion of the object and defines possible locations of the target based upon previous locations. The observation model provides a measure of the distance of an observation of the object relative to the current Eigenbasis. The inference model predicts the most likely location of the object based upon past and present observations. The method is effective with or without training samples. A computer-based system provides a means for implementing the method. The effectiveness of the system and method are demonstrated through simulation.
摘要翻译： 公开了用于运动视频内的对象的自适应概率跟踪的系统和方法。 该方法利用时变特征向量和动态，观察和推理模型。 Eigenbasis作为目标对象的模型。 动态模型表示对象的运动，并根据先前的位置定义目标的可能位置。 观察模型提供了对象相对于当前Eigenbasis的观察距离的度量。 推论模型基于过去和现在的观察预测对象的最可能的位置。 该方法在有或没有训练样本的情况下是有效的。 基于计算机的系统提供了实现该方法的手段。 通过仿真证明了系统和方法的有效性。
-
5.发明授权Monocular tracking of 3D human motion with a coordinated mixture of factor analyzers 有权单因素跟踪3D人体运动与因子分析仪的协调混合公开(公告)号：US07450736B2
公开(公告)日：2008-11-11
申请号：US11553382
申请日：2006-10-26
申请人： Ming-Hsuan Yang , Rui Li
发明人： Ming-Hsuan Yang , Rui Li
IPC分类号： G06K9/00
CPC分类号： G06K9/00342 , G06K9/6232 , G06K9/6252 , G06T7/20
摘要： Disclosed is a method and system for efficiently and accurately tracking three-dimensional (3D) human motion from a two-dimensional (2D) video sequence, even when self-occlusion, motion blur and large limb movements occur. In an offline learning stage, 3D motion capture data is acquired and a prediction model is generated based on the learned motions. A mixture of factor analyzers acts as local dimensionality reducers. Clusters of factor analyzers formed within a globally coordinated low-dimensional space makes it possible to perform multiple hypothesis tracking based on the distribution modes. In the online tracking stage, 3D tracking is performed without requiring any special equipment, clothing, or markers. Instead, motion is tracked in the dimensionality reduced state based on a monocular video sequence.
摘要翻译： 公开了一种用于从二维（2D）视频序列高效地和准确地跟踪三维（3D）人体运动的方法和系统，即使当发生自闭塞，运动模糊和大肢体运动时也是如此。 在离线学习阶段，获取3D运动捕捉数据，并根据学习动作生成预测模型。 因子分析仪的混合物作为局部维数减少剂。 在全球协调的低维空间内形成的因子分析器群集使得可以基于分布模式执行多个假设跟踪。 在线跟踪阶段，不需要任何特殊的设备，衣物或标记就可进行3D跟踪。 相反，基于单眼视频序列在维度降低状态下跟踪运动。
-
6.发明授权公开(公告)号：US07248738B2
公开(公告)日：2007-07-24
申请号：US10989967
申请日：2004-11-15
申请人： Jeffrey Ho , Jongwoo Lim , Ming-Hsuan Yang
发明人： Jeffrey Ho , Jongwoo Lim , Ming-Hsuan Yang
IPC分类号： G06K9/62
CPC分类号： G06K9/6224 , G06K9/6215 , G06K9/6252
摘要： A system and a method are disclosed for clustering images of objects seen from different viewpoints. That is, given an unlabelled set of images of n objects, an unsupervised algorithm groups the images into N disjoint subsets such that each subset only contains images of a single object. The clustering method makes use of a broad geometric framework that exploits the interplay between the geometry of appearance manifolds and the symmetry of the 2D affine group.
摘要翻译： 公开了一种系统和方法，用于对从不同视点看到的对象的图像进行聚类。 也就是说，给定n个对象的未标记的图像集合，无监督的算法将图像分组为N个不相交的子集，使得每个子集仅包含单个对象的图像。 聚类方法利用了广泛的几何框架，利用了外观多样性的几何和2D仿射组的对称性之间的相互作用。
-
公开(公告)号：US20070098254A1
公开(公告)日：2007-05-03
申请号：US11553388
申请日：2006-10-26
CPC分类号： G06K9/4647 , G06K9/00369
摘要： A method and system efficiently and accurately detects humans in a test image and classifies their pose. In a training stage, a probabilistic model is derived in an unsupervised or semi-supervised manner such that at least some poses are not manually labeled. The model provides two sets of model parameters to describe the statistics of images containing humans and images of background scenes. In a testing stage, the probabilistic model is used to determine if a human is present in the image, and classify the human's pose based on the poses in the training images. A solution is efficiently provided to both human detection and pose classification by using the same probabilistic model to solve the problems.
摘要翻译： 一种方法和系统有效和准确地检测测试图像中的人类并对其姿态进行分类。 在训练阶段，以无监督或半监督的方式导出概率模型，使得至少一些姿势不是手动标记的。 该模型提供两组模型参数来描述包含人类和背景场景图像的图像的统计。 在测试阶段，概率模型用于确定人物是否存在于图像中，并且基于训练图像中的姿态对人的姿势进行分类。 通过使用相同的概率模型来解决问题，有效地提供了人类检测和姿态分类的解决方案。
-
公开(公告)号：US20050180627A1
公开(公告)日：2005-08-18
申请号：US10858930
申请日：2004-06-01
申请人： Ming-Hsuan Yang , Jongwoo Lim , David Ross , Takahiro Ohashi
发明人： Ming-Hsuan Yang , Jongwoo Lim , David Ross , Takahiro Ohashi
CPC分类号： G06K9/6269 , G06K9/00228 , G06K9/6857
摘要： The face detection system and method attempts classification of a test image before performing all of the kernel evaluations. Many subimages are not faces and should be relatively easy to identify as such. Thus, the SVM classifier try to discard non-face images using as few kernel evaluations as possible using a cascade SVM classification. In the first stage, a score is computed for the first two support vectors, and the score is compared to a threshold. If the score is below the threshold value, the subimage is classified as not a face. If the score is above the threshold value, the cascade SVM classification function continues to apply more complicated decision rules, each time doubling the number of kernel evaluations, classifying the image as a non-face (and thus terminating the process) as soon as the test image fails to satisfy one of the decision rules. Finally, if the subimage has satisfied all intermediary decision rules, and has now reached the point at which all support vectors must be considered, the original decision function is applied. Satisfying this final rule, and all intermediary rules, is the only way for a test image to garner a positive (face) classification.
摘要翻译： 面部检测系统和方法在执行所有内核评估之前尝试对测试图像进行分类。 许多子图像不是面孔，应该比较容易识别。 因此，SVM分类器尝试使用级联SVM分类使用尽可能少的内核评估来丢弃非面部图像。 在第一阶段，对前两个支持向量计算分数，并将分数与阈值进行比较。 如果分数低于阈值，则子图像被分类为不是脸部。 如果分数高于阈值，则级联SVM分类功能继续应用更复杂的决策规则，每次将内核评估的数量加倍，将图像分类为非面（并因此终止进程），一旦 测试图像不能满足其中一个决策规则。 最后，如果子图像满足了所有的中介决策规则，并且现在已经到了必须考虑所有支持向量的点，则应用原始决策函数。 满足这个最终规则和所有中介规则是测试图像获得积极（面部）分类的唯一方法。
-
公开(公告)号：US07499574B1
公开(公告)日：2009-03-03
申请号：US10703288
申请日：2003-11-06
申请人： Ming-Hsuan Yang , Jeffrey Ho , Kuang-Chih Lee
发明人： Ming-Hsuan Yang , Jeffrey Ho , Kuang-Chih Lee
IPC分类号： G06K9/00
CPC分类号： G06K9/00335 , G06K9/00275 , G06K9/00288 , G06K9/3208 , G06K9/6232
摘要： The present invention meets these needs by providing temporal coherency to recognition systems. One embodiment of the present invention comprises a manifold recognition module to use a sequence of images for recognition. A manifold training module receives a plurality of training image sequences (e.g. from a video camera), each training image sequence including an individual in a plurality of poses, and establishes relationships between the images of a training image sequence. A probabilistic identity module receives a sequence of recognition images including a target individual for recognition, and identifies the target individual based on the relationship of training images corresponding to the recognition images. An occlusion module masks occluded portions of an individual's face to prevent distorted identifications.
摘要翻译： 本发明通过向识别系统提供时间一致性来满足这些需求。 本发明的一个实施例包括使用一系列图像进行识别的歧管识别模块。 歧管训练模块接收多个训练图像序列（例如，从摄像机），每个训练图像序列包括多个姿势中的个体，并且建立训练图像序列的图像之间的关系。 概率识别模块接收包括用于识别的目标个体的识别图像序列，并且基于与识别图像相对应的训练图像的关系来识别目标个体。 闭塞模块遮挡个人脸部的遮挡部分以防止变形的识别。
-
10.发明申请Fast Human Pose Estimation Using Appearance And Motion Via Multi-Dimensional Boosting Regression 有权通过多维增强回归的外观和运动的快速人体姿态估计公开(公告)号：US20080137956A1
公开(公告)日：2008-06-12
申请号：US11950662
申请日：2007-12-05
IPC分类号： G06K9/00
CPC分类号： G06K9/00342 , G06K9/6257
摘要： Methods and systems are described for three-dimensional pose estimation. A training module determines a mapping function between a training image sequence and pose representations of a subject in the training image sequence. The training image sequence is represented by a set of appearance and motion patches. A set of filters are applied to the appearance and motion patches to extract features of the training images. Based on the extracted features, the training module learns a multidimensional mapping function that maps the motion and appearance patches to the pose representations of the subject. A testing module outputs a fast human pose estimation by applying the learned mapping function to a test image sequence.
摘要翻译： 描述了用于三维姿态估计的方法和系统。 训练模块确定训练图像序列和训练图像序列中的对象的姿态表示之间的映射函数。 训练图像序列由一组外观和运动补丁表示。 将一组滤镜应用于外观和运动补片以提取训练图像的特征。 基于提取的特征，训练模块学习将运动和外观补片映射到对象的姿态表示的多维映射函数。 测试模块通过将学习的映射函数应用于测试图像序列来输出快速人体姿态估计。
-
-
-
-
-
-
-
-
-
搜索结果
36 条记录 (耗时 0.02 秒)
