公开(公告)号：US20070286514A1

公开(公告)日：2007-12-13

申请号：US11450796

申请日：2006-06-08

Applicant: Michael Scott Brown ,  Tat Jen Cham ,  Peng Song

Inventor： Michael Scott Brown ,  Tat Jen Cham ,  Peng Song

IPC: G06K9/40

CPC classification number: H04N9/3194 ,  H04N9/3102 ,  H04N9/3179

Abstract: A method for minimizing image blur in an image projected onto a display surface by a projector, the image blur being caused by out-of-focus regions, the method comprising: estimating (10) a spatially varying point-spread-functions (PSF) profile for a test image projected by the projector; and pre-conditioning (11) the image using a predetermined pre-processing algorithm based on the estimated PSF profile; wherein the pre-conditioned image is projected (17) by the projector to minimise image blur.

Abstract translation: 一种用于使由投影仪投影到显示表面上的图像中的图像模糊最小化的方法，所述图像模糊是由焦点外区域引起的，所述方法包括：估计（10）空间变化点扩展函数（PSF） 由投影仪投影的测试图像的轮廓; 并且使用基于所估计的PSF简档的预定预处理算法预处理（11）图像; 其中所述预处理图像由所述投影仪投影（17）以最小化图像模糊。

公开(公告)号：US07006055B2

公开(公告)日：2006-02-28

申请号：US09997419

申请日：2001-11-29

Applicant: Rahul Sukthankar ,  Tat-Jen Cham ,  Gita R. Sukthankar ,  James M. Rehg

Inventor： Rahul Sukthankar ,  Tat-Jen Cham ,  Gita R. Sukthankar ,  James M. Rehg

IPC: G09G5/00

CPC classification number: G09G3/002 ,  G06F3/1423

Abstract: Media slides are often employed in conference sessions, meetings, lectures, and other interactive forums. The proliferation of laptops and handheld computers allows a speaker to present directly from the laptop by connecting to the projector at the conference site. Physically connecting and disconnecting each presenter's laptop to the projection apparatus, however, can be a clumsy and disruptive process, particularly since the presenters may be seated at various locations around the room. A wireless interface between a presentation server and a laptop in a multi-user multi-projector presentation system allows a media sequence from each media source to be displayed on a common display via the presentation server and the wireless interface. Presenters need not run or swap cables or other physical connections to switch media sources to the common display. The interface requires no software modification to the media source laptops and maintains independence between media sources and the server for security. The presentation server communicates with the media sources over the mouse port allowing innovative user interfaces, such as gesture recognition, to be employed for presentation control without additional software. Multiple projectors redundantly illuminate the display surface, dynamically eliminating shadows and other display artifacts when presenters walk between a projector and the screen. Distracting projected light cast on to the occluding presenters is automatically suppressed.

公开(公告)号：US06795567B1

公开(公告)日：2004-09-21

申请号：US09565414

申请日：2000-05-05

Applicant: Tat-Jen Cham ,  James Matthew Rehg

Inventor： Tat-Jen Cham ,  James Matthew Rehg

IPC: G06K900

CPC classification number: G06T7/251 ,  G06T7/246 ,  G06T2207/10016 ,  G06T2207/30196

Abstract: 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.

Abstract translation: 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。

公开(公告)号：US06618490B1

公开(公告)日：2003-09-09

申请号：US09466975

申请日：1999-12-20

Applicant: Tat-Jen Cham ,  James Matthew Rehg

Inventor： Tat-Jen Cham ,  James Matthew Rehg

IPC: G06K900

CPC classification number: G06K9/00335 ,  G06T7/251 ,  G06T2207/10016 ,  G06T2207/30196 ,  Y10S707/99933

Abstract: 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.

Abstract translation: 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。

公开(公告)号：US06597801B1

公开(公告)日：2003-07-22

申请号：US09466970

申请日：1999-12-20

Applicant: Tat-Jen Cham ,  James Matthew Rehg

Inventor： Tat-Jen Cham ,  James Matthew Rehg

IPC: G06K900

CPC classification number: G06K9/00369 ,  G06K9/6206 ,  G06K9/6857 ,  G06K9/6892 ,  G06T7/251 ,  G06T7/277 ,  G06T2207/10016 ,  G06T2207/30196

Abstract: 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.

Abstract translation: 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。

公开(公告)号：US06314204B1

公开(公告)日：2001-11-06

申请号：US09185280

申请日：1998-11-03

Applicant: Tat-Jen Cham ,  James Matthew Rehg

Inventor： Tat-Jen Cham ,  James Matthew Rehg

IPC: G06K900

CPC classification number: G06K9/6278 ,  G06K9/6206 ,  G06K9/6226 ,  G06T7/277

Abstract: 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.

公开(公告)号：US06226409B1

公开(公告)日：2001-05-01

申请号：US09185279

申请日：1998-11-03

Applicant: Tat-Jen Cham ,  James Matthew Rehg

Inventor： Tat-Jen Cham ,  James Matthew Rehg

IPC: G06K962

CPC classification number: G06K9/6217

Abstract: 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.

公开(公告)号：US06353679B1

公开(公告)日：2002-03-05

申请号：US09185278

申请日：1998-11-03

Applicant: Tat-Jen Cham ,  James Matthew Rehg

Inventor： Tat-Jen Cham ,  James Matthew Rehg

IPC: G06K900

CPC classification number: G06K9/6226 ,  G06T7/277

Abstract: 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.