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1. 发明授权

US06314204B1 Multiple mode probability density estimation with application to multiple hypothesis tracking 有权
公开(公告)号：US06314204B1
公开(公告)日：2001-11-06
申请号：US09185280
申请日：1998-11-03
申请人： Tat-Jen Cham , James Matthew Rehg
发明人： Tat-Jen Cham , James Matthew Rehg
IPC分类号： G06K900
CPC分类号： G06K9/6278 , G06K9/6206 , G06K9/6226 , G06T7/277
摘要： The invention recognizes that a probability density function for fitting a model to a complex set of data often has multiple modes, each mode representing a reasonably probable state of the model when compared with the data. Particularly, sequential data such as are collected from detection of moving objects in three dimensional space are placed into data frames. Computation of the probability density function of the model state involves two main stages: (1) state prediction, in which the prior probability distribution is generated from information known prior to the availability of the data, and (2) state update, in which the posterior probability distribution is formed by updating the prior distribution with information obtained from observing the data. In particular this information obtained purely from data observations can also be expressed as a probability density function, known as the likelihood function. The likelihood function is a multimodal (multiple peaks) function when a single data frame leads to multiple distinct measurements from which the correct measurement associated with the model cannot be distinguished. The invention analyzes a multimodal likelihood function by numerically searching the likelihood function for peaks. The numerical search proceeds by randomly sampling from the prior distribution to select a number of seed points in state-space, and then numerically finding the maxima of the likelihood function starting from each seed point. Furthermore, kernel functions are fitted to these peaks to represent the likelihood function as an analytic function. The resulting posterior distribution is also multimodal and represented using a set of kernel functions. It is computed by combining the prior distribution and the likelihood function using Bayes Rule. The peaks in the posterior distribution are also referred to as ‘hypotheses’, as they are hypotheses for the states of the model which best explain both the data and the prior knowledge.

2. 发明授权

US06353679B1 Sample refinement method of multiple mode probability density estimation 失效
公开(公告)号：US06353679B1
公开(公告)日：2002-03-05
申请号：US09185278
申请日：1998-11-03
申请人： Tat-Jen Cham , James Matthew Rehg
发明人： Tat-Jen Cham , James Matthew Rehg
IPC分类号： G06K900
CPC分类号： G06K9/6226 , G06T7/277
摘要： The invention recognizes that a probability density function for fitting a model to a complex set of data often has multiple modes, each mode representing a reasonably probable state of the model when compared with the data. Particularly, sequential data such as are collected from detection of moving objects in three dimensional space are placed into data frames. Also, a single frame of data may require analysis by a sequence of analysis operations. Computation of the probability density function of the model state involves two main stages: (1) state prediction, in which the prior probability distribution is generated from information known prior to the availability of the data, and (2) state update, in which the posterior probability distribution is formed by updating the prior distribution with information obtained from observing the data. In particular this information obtained purely from data observations can also be expressed as a probability density function, known as the likelihood function. The likelihood function is a multimodal (multiple peaks) function when a single data frame leads to multiple distinct measurements from which the correct measurement associated with the model cannot be distinguished. The invention analyzes a multimodal likelihood function by numerically searching the likelihood function for peaks. The numerical search proceeds by randomly sampling from the prior distribution to select a number of seed points in state-space, and then numerically finding the maxima of the likelihood function starting from each seed point. Furthermore, kernel functions are fitted to these peaks to represent the likelihood function as an analytic function. The resulting posterior distribution is also multimodal and represented using a set of kernel functions. It is computed by combining the prior distribution and the likelihood function using Bayes Rule. The peaks in the posterior distribution are also referred to as ‘hypotheses’, as they are hypotheses for the states of the model which best explain both the data and the prior knowledge.

3. 发明授权

US06795567B1 Method for efficiently tracking object models in video sequences via dynamic ordering of features 失效
标题翻译：通过特征的动态排序有效地跟踪视频序列中的对象模型的方法
公开(公告)号：US06795567B1
公开(公告)日：2004-09-21
申请号：US09565414
申请日：2000-05-05
申请人： Tat-Jen Cham , James Matthew Rehg
发明人： Tat-Jen Cham , James Matthew Rehg
IPC分类号： G06K900
CPC分类号： G06T7/251 , G06T7/246 , G06T2207/10016 , G06T2207/30196
摘要： An object model has a plurality of features and is described by a model state. An unregistered feature of the object model, and an available frame from a sequence of images are selected to minimize a cost function of a subsequent search for a match of the selected model feature to the image in the selected frame. Upon a match, the feature is registered in that frame. The model state is then updated for each available frame. The steps of selecting, searching and updating are repeated. A video storage module may contain only one frame corresponding to a single time instance, in which case the framework used is based on integrated sequential feature selection. Alternatively, the video store may contain the entire video sequence, in which case feature selection is performed across all video frames for maximum tracking efficiency. Finally, the video store may contain a small number of previous frames plus the current frame, in which case feature selection spans only a subset of the entire video sequence for each feature matching cycle.
摘要翻译：对象模型具有多个特征并且由模型状态描述。选择对象模型的未注册特征以及来自图像序列的可用帧以最小化后续搜索所选择的模型特征与所选择的帧中的图像的匹配的成本函数。匹配后，该功能将在该帧中注册。然后，为每个可用帧更新模型状态。重复选择，搜索和更新的步骤。视频存储模块可以仅包含与单个时间实例对应的一个帧，在这种情况下，所使用的框架基于集成的顺序特征选择。或者，视频存储器可以包含整个视频序列，在这种情况下，跨所有视频帧执行特征选择以获得最大的跟踪效率。最后，视频商店可以包含少量先前帧加上当前帧，在这种情况下，特征选择仅跨越每个特征匹配周期的整个视频序列的子集。

4. 发明授权

US06618490B1 Method for efficiently registering object models in images via dynamic ordering of features 失效
标题翻译：通过特征的动态排序有效地在图像中注册对象模型的方法
公开(公告)号：US06618490B1
公开(公告)日：2003-09-09
申请号：US09466975
申请日：1999-12-20
申请人： Tat-Jen Cham , James Matthew Rehg
发明人： Tat-Jen Cham , James Matthew Rehg
IPC分类号： G06K900
CPC分类号： G06K9/00335 , G06T7/251 , G06T2207/10016 , G06T2207/30196 , Y10S707/99933
摘要： An object model, having a plurality of features and described by a model state, is registered in an image. Unregistered features of the object model are dynamically selected such that the cost function of each feature search is minimized. A search is performed for a match of the selected model feature to the image, or to features within the image, to register the feature, and the model state is updated accordingly. These steps are repeated until all features have been registered. The search is performed in a region of high probability of a match. The cost function for a feature is based on the feature's basin of attraction, and in particular can be based on the complexity of the search process at each basin of attraction. A search region is based on a projected state probability distribution. In particular, the cost function is based on the “matching ambiguity,” or the number of search operations required to find a true match with some specified minimum probability. For feature-to-feature matching, the number of search operations is preferably the number of target features located within each search region. For feature-to-image matching, the matching ambiguity is computed, for each search region, by dividing the region into minimally-overlapping volumes which have the same size and shape as a basin of attraction associated with the feature, and then counting the number of volumes required to cover the regions. The model state is updated according to a propagated state probability distribution. Preferably, the propagation of the probability distribution is based on successive registered features.
摘要翻译：具有多个特征并由模型状态描述的对象模型被登记在图像中。动态地选择对象模型的未注册特征，使得每个特征搜索的成本函数被最小化。对所选择的模型特征与图像或图像内的特征的匹配执行搜索以注册该特征，并且相应地更新模型状态。重复这些步骤，直到所有功能都已注册。搜索在匹配概率高的区域中进行。功能的成本函数是基于特征的吸引力盆地，特别是可以基于每个吸引力盆地的搜索过程的复杂性。搜索区域基于投影状态概率分布。特别地，成本函数基于“匹配模糊度”或者找到具有一些指定的最小概率的真实匹配所需的搜索操作的数量。对于特征到特征匹配，搜索操作的数量优选地是位于每个搜索区域内的目标特征的数量。对于特征到图像匹配，对于每个搜索区域，对于每个搜索区域，计算匹配的模糊度，将区域划分成具有与特征相关联的吸引盆地相同的大小和形状的最小重叠的体积，然后计数覆盖区域所需的数量。模型状态根据传播的状态概率分布进行更新。优选地，概率分布的传播基于连续的登记特征。

5. 发明授权

US06597801B1 Method for object registration via selection of models with dynamically ordered features 失效
标题翻译：通过选择具有动态有序特征的模型进行对象注册的方法
公开(公告)号：US06597801B1
公开(公告)日：2003-07-22
申请号：US09466970
申请日：1999-12-20
申请人： Tat-Jen Cham , James Matthew Rehg
发明人： Tat-Jen Cham , James Matthew Rehg
IPC分类号： G06K900
CPC分类号： G06K9/00369 , G06K9/6206 , G06K9/6857 , G06K9/6892 , G06T7/251 , G06T7/277 , G06T2207/10016 , G06T2207/30196
摘要： A plurality of object models, where each object model comprises a plurality of features and is described by a model state, are registered in at least one image a subset of the object models is selected. Different object models have different sets of features, which may or may not overlap. A feature of each selected object model is registered in one of the images, and the model state for each selected object model is updated accordingly. The model states of some or all of the object models are then updated according to a set of constraints. These steps are repeated until one or more object models are registered. At the beginning of each registration cycle, a cost function of a subsequent search is determined for each unregistered feature of each object model. An unregistered feature of each object model is then selected such that the cost function is minimized. Object models to which the selected features belong are then selected, and each selected object model's selected feature is registered by matching it to an image. The selected unregistered features are ranked according to some criterion, such as the number of operations needed to search for a feature, i.e., the matching ambiguity. Object models are then selected according to the ranking. Preferably, a predetermined number of object models is selected each cycle.
摘要翻译：多个对象模型（其中每个对象模型包括多个特征并且由模型状态描述）被登记在至少一个图像中，对象模型的子集被选择。不同的对象模型具有不同的特征集合，其可以或可以不重叠。将每个所选对象模型的特征登记在其中一个图像中，并且相应地更新每个所选对象模型的模型状态。然后根据一组约束更新一些或所有对象模型的模型状态。重复这些步骤直到一个或多个对象模型被注册。在每个注册周期开始时，为每个对象模型的每个未注册特征确定后续搜索的成本函数。然后选择每个对象模型的未注册的特征，使得成本函数被最小化。然后选择所选特征所属的对象模型，并且通过将所选特征与图像匹配来注册每个所选择的对象模型的所选特征。所选择的未登记特征根据某些标准进行排序，例如搜索特征所需的操作数，即匹配模糊度。然后根据排名选择对象模型。优选地，每个周期选择预定数量的对象模型。

6. 发明授权

US06226409B1 Multiple mode probability density estimation with application to sequential markovian decision processes 失效
公开(公告)号：US06226409B1
公开(公告)日：2001-05-01
申请号：US09185279
申请日：1998-11-03
申请人： Tat-Jen Cham , James Matthew Rehg
发明人： Tat-Jen Cham , James Matthew Rehg
IPC分类号： G06K962
CPC分类号： G06K9/6217
摘要： The invention recognizes that a probability density function for fitting a model to a complex set of data often has multiple modes, each mode representing a reasonably probable state of the model when compared with the data. Particularly, an image may require a complex sequence of analyses in order for a pattern embedded in the image to be ascertained. Computation of the probability density function of the model state involves two main stages: (1) state prediction, in which the prior probability distribution is generated from information known prior to the availability of the data, and (2) state update, in which the posterior probability distribution is formed by updating the prior distribution with information obtained from observing the data. In particular this information obtained purely from data observations can also be expressed as a probability density function, known as the likelihood function. The likelihood function is a multimodal (multiple peaks) function when a single data frame leads to multiple distinct measurements from which the correct measurement associated with the model cannot be distinguished. The invention analyzes a multimodal likelihood function by numerically searching the likelihood function for peaks. The numerical search proceeds by randomly sampling from the prior distribution to select a number of seed points in state-space, and then numerically finding the maxima of the likelihood function starting from each seed point. Furthermore, kernel functions are fitted to these peaks to represent the likelihood function as an analytic function. The resulting posterior distribution is also multimodal and represented using a set of kernel functions. It is computed by combining the prior distribution and the likelihood function using Bayes Rule. The peaks in the posterior distribution are also referred to as ‘hypotheses’, as they are hypotheses for the states of the model which best explain both the data and the prior knowledge.

7. 发明授权

US06683968B1 Method for visual tracking using switching linear dynamic system models 失效
公开(公告)号：US06683968B1
公开(公告)日：2004-01-27
申请号：US09654022
申请日：2000-09-01
申请人： Vladimir Pavlović , James Matthew Rehg
发明人： Vladimir Pavlović , James Matthew Rehg
IPC分类号： G06K900
CPC分类号： G06K9/6297 , G06K9/00335
摘要： A target in a sequence of measurements is tracked by modeling the target with a switching linear dynamic system (SLDS) having a plurality of dynamic models. Each dynamic model is associated with a switching state such that a model is selected when its associated switching state is true. A set of continuous state estimates is determined for a given measurement, and for each possible switching state. A state transition record is then determined by determining and recording, for a given measurement and for each possible switching state, an optimal previous switching state, based on the measurement sequence, where the optimal previous switching state optimizes a transition probability based on the set of continuous state estimates. A measurement model of the target is fitted to the measurement sequence. The measurement model is the description of the influence of the state on the measurement. It couples what is observed to the estimated target. Finally, a trajectory of the target is estimated from the measurement model fitting, the state transition record and parameters of the SLDS, where the estimated trajectory is a sequence of continuous state estimates of the target which correspond to the measurement sequence. The set of continuous state estimates is preferably obtained through Viterbi prediction. The optimal previous switching state can be an optimal prior switching state, or can be an optimal posterior switching state.

8. 发明授权

US06243106B1 Method for figure tracking using 2-D registration and 3-D reconstruction 失效
标题翻译：使用二维配准和三维重建的图形跟踪方法
公开(公告)号：US06243106B1
公开(公告)日：2001-06-05
申请号：US09059197
申请日：1998-04-13
申请人： James Matthew Rehg , Daniel D. Morris
发明人： James Matthew Rehg , Daniel D. Morris
IPC分类号： G06K900
CPC分类号： G06K9/00369 , G06T7/246
摘要： In a computerized method, a moving articulated figure is tracked in a sequence of 2-D images measured by a monocular camera. The images are individually registered with each other using a 2-D scaled prismatic model of the figure. The 2-D model includes a plurality of links connected by revolute joints to form is a branched, linear-chain of connected links. The registering produces a state trajectory for the figure in the sequence of images. During a reconstructing step, a 3-D model is fitted to the state trajectory to estimate kinematic parameters, and the estimated kinematic parameters are refined using an expectation maximization technique.
摘要翻译：在计算机化方法中，以单目相机测量的2-D图像序列跟踪移动铰接图。使用该图的二维缩放棱镜模型将图像彼此单独地注册。 2-D模型包括通过旋转接头连接以形成的多个连接件，是连接的分支的直链链。记录在图像序列中产生图形的状态轨迹。在重建步骤中，将3-D模型拟合到状态轨迹以估计运动学参数，并且使用期望最大化技术来改进估计的运动学参数。

9. 发明授权

US06240198B1 Method for figure tracking using 2-D registration 失效
标题翻译：使用二维配准的图形跟踪方法
公开(公告)号：US06240198B1
公开(公告)日：2001-05-29
申请号：US09059478
申请日：1998-04-13
申请人： James Matthew Rehg , Daniel D. Morris
发明人： James Matthew Rehg , Daniel D. Morris
IPC分类号： G06K900
CPC分类号： G06K9/00369 , G06T7/246
摘要： In a computerized method, a moving articulated figure is tracked in a sequence of 2-D images measured by a monocular camera. The images are individually registered with each other using a 2-D scaled prismatic model of the figure. The 2-D model includes a plurality of links connected by revolute joints to form is a branched, linear-chain of connected links. The registering produces a state trajectory for the figure in the sequence of images. During a reconstructing step, a 3-D model is fitted to the state trajectory to estimate kinematic parameters, and the estimated kinematic parameters are refined using an expectation maximization technique.
摘要翻译：在计算机化方法中，以单目相机测量的2-D图像序列跟踪移动铰接图。使用该图的二维缩放棱镜模型将图像彼此单独地注册。 2-D模型包括通过旋转接头连接以形成的多个连接件，是连接的分支的直链链。记录在图像序列中产生图形的状态轨迹。在重建步骤中，将3-D模型拟合到状态轨迹以估计运动学参数，并且使用期望最大化技术来改进估计的运动学参数。

10. 发明授权

US06694044B1 Method for motion classification using switching linear dynamic system models 失效
公开(公告)号：US06694044B1
公开(公告)日：2004-02-17
申请号：US09654300
申请日：2000-09-01
申请人： Vladimir Pavlović , James Matthew Rehg
发明人： Vladimir Pavlović , James Matthew Rehg
IPC分类号： G06K900
CPC分类号： G05B17/02 , G06K9/00335
摘要： Portions of an input measurement sequence are classified into a plurality of regimes by associating each of a plurality of dynamic models with one a switching state such that a model is selected when its associated switching state is true. In a Viterbi-based method, a state transition record is determined, based on the input sequence. A switching state sequence is determined by backtracking through the state transition record. Finally, portions of the input sequence are classified into different regimes, responsive to the switching state sequence. In a variational-based method, the switching state at a particular instance is also determined by a switching model. The dynamic model is then decoupled from the switching model. Parameters of the decoupled dynamic model are determined responsive to a switching state probability estimate. A state of the decoupled dynamic model corresponding to a measurement at the particular instance is estimated, responsive to the input sequence. Parameters of the decoupled switching model are then determined responsive to the dynamic state estimate. A probability is estimated for each possible switching state of the decoupled switching model. A switching state sequence is determined based on the estimated switching state probabilities. Finally, portions of the input sequence are classified into different regimes, responsive to the determined switching state sequence.

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