公开(公告)号：US07369682B2

公开(公告)日：2008-05-06

申请号：US11179881

申请日：2005-07-11

申请人： Ming-Hsuan Yang ,  Ruei-Sung Lin ,  Jongwoo Lim ,  David Ross

发明人： Ming-Hsuan Yang ,  Ruei-Sung Lin ,  Jongwoo Lim ,  David Ross

IPC分类号： G06K9/00

CPC分类号： G06K9/00241 ,  G06K9/00261 ,  G06K9/3233 ,  G06K9/6214 ,  G06K9/6234 ,  G06K9/6247 ,  G06T7/246 ,  G06T7/277 ,  G06T7/70 ,  G06T2207/30241

摘要： A system and a method are disclosed for an adaptive discriminative generative model with a probabilistic interpretation. As applied to visual tracking, the discriminative generative model separates the target object from the background more accurately and efficiently than conventional methods. A computationally efficient algorithm constantly updates the discriminative model over time. The discriminative generative model adapts to accommodate dynamic appearance variations of the target and background. Experiments show that the discriminative generative model effectively tracks target objects undergoing large pose and lighting changes.

摘要翻译： 公开了一种具有概率解释的自适应识别生成模型的系统和方法。 应用于视觉跟踪，鉴别生成模型比传统方法更准确有效地将目标对象与背景分离。 计算有效的算法随着时间不断更新辨别模型。 鉴别生成模型适应于适应目标和背景的动态外观变化。 实验表明，识别性生成模型有效地跟踪目标物体的大姿态和照明变化。

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。

公开(公告)号：US20050175219A1

公开(公告)日：2005-08-11

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 ,  G06K9/46 ,  G06T7/20

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。

公开(公告)号：US20060036399A1

公开(公告)日：2006-02-16

申请号：US11179881

申请日：2005-07-11

申请人： Ming-Hsuan Yang ,  Ruei-Sung Lin ,  Jongwoo Lim ,  David Ross

发明人： Ming-Hsuan Yang ,  Ruei-Sung Lin ,  Jongwoo Lim ,  David Ross

IPC分类号： G06F17/18

CPC分类号： G06K9/00241 ,  G06K9/00261 ,  G06K9/3233 ,  G06K9/6214 ,  G06K9/6234 ,  G06K9/6247 ,  G06T7/246 ,  G06T7/277 ,  G06T7/70 ,  G06T2207/30241

摘要： A system and a method are disclosed for an adaptive discriminative generative model with a probabilistic interpretation. As applied to visual tracking, the discriminative generative model separates the target object from the background more accurately and efficiently than conventional methods. A computationally efficient algorithm constantly updates the discriminative model over time. The discriminative generative model adapts to accommodate dynamic appearance variations of the target and background. Experiments show that the discriminative generative model effectively tracks target objects undergoing large pose and lighting changes.

摘要翻译： 公开了一种具有概率解释的自适应识别生成模型的系统和方法。 应用于视觉跟踪，鉴别生成模型比传统方法更准确有效地将目标对象与背景分离。 计算有效的算法随着时间不断更新辨别模型。 鉴别生成模型适应于适应目标和背景的动态外观变化。 实验表明，识别性生成模型有效地跟踪目标物体的大姿态和照明变化。

公开(公告)号：US07430315B2

公开(公告)日：2008-09-30

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

IPC分类号： G06K9/62 ,  G06K9/64 ,  G06K9/40

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：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

IPC分类号： G06K9/00 ,  G06K9/62 ,  G06K9/68 ,  G06K9/74

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：US07224831B2

公开(公告)日：2007-05-29

申请号：US10858878

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

IPC分类号： G06K9/36

CPC分类号： G06K9/00201

摘要： The advantage of the present invention is to appropriately detect the object. The object detection apparatus in the present invention has a plurality of cameras to determine the distance to the objects, a distance determination unit to determine the distance therein, a histogram generation unit to specify the frequency of the pixels against the distances to the pixels, an object distance determination unit that determines the most likely distance, a probability mapping unit that provides the probabilities of the pixels based on the difference of the distance, a kernel detection unit that determines a kernel region as a group of the pixels, a periphery detection unit that determines a peripheral region as a group of the pixels, selected from the pixels being close to the kernel region and an object specifying unit that specifies the object region where the object is present with a predetermined probability.

摘要翻译： 本发明的优点是适当地检测物体。 本发明的物体检测装置具有多个照相机，用于确定与物体的距离，距离确定单元，用于确定其中的距离;直方图生成单元，用于根据与像素的距离来指定像素的频率; 确定最可能的距离的对象距离确定单元，基于距离差提供像素概率的概率映射单元，将核区域确定为像素组的内核检测单元，周边检测单元 将外围区域确定为从接近核心区域的像素中选择的像素组，以及以预定概率指定对象存在的对象区域的对象指定单元。

公开(公告)号：US20050180602A1

公开(公告)日：2005-08-18

申请号：US10858878

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

IPC分类号： G06K9/00 ,  G06K9/36 ,  G06K9/48

CPC分类号： G06K9/00201

摘要： The advantage of the present invention is to appropriately detect the object. The object detection apparatus in the present invention has a plurality of cameras to determine the distance to the objects, a distance determination unit to determine the distance therein, a histogram generation unit to specify the frequency of the pixels against the distances to the pixels, an object distance determination unit that determines the most likely distance, a probability mapping unit that provides the probabilities of the pixels based on the difference of the distance, a kernel detection unit that determines a kernel region as a group of the pixels, a periphery detection unit that determines a peripheral region as a group of the pixels, selected from the pixels being close to the kernel region and an object specifying unit that specifies the object region where the object is present with a predetermined probability.

摘要翻译： 本发明的优点是适当地检测物体。 本发明的物体检测装置具有多个照相机，用于确定与物体的距离，距离确定单元，用于确定其中的距离;直方图生成单元，用于根据与像素的距离来指定像素的频率; 确定最可能的距离的对象距离确定单元，基于距离差提供像素概率的概率映射单元，将核区域确定为像素组的内核检测单元，周边检测单元 将外围区域确定为从接近核心区域的像素中选择的像素组，以及以预定概率指定对象存在的对象区域的对象指定单元。

公开(公告)号：US08676725B1

公开(公告)日：2014-03-18

申请号：US12794380

申请日：2010-06-04

申请人： Ruei-Sung Lin ,  David Ross ,  Jay Yagnik

发明人： Ruei-Sung Lin ,  David Ross ,  Jay Yagnik

IPC分类号： G06F15/18

CPC分类号： G06N99/005

摘要： Methods, systems and articles of manufacture for identifying semantic nearest neighbors in a feature space are described herein. A method embodiment includes generating an affinity matrix for objects in a given feature space, wherein the affinity matrix identifies the semantic similarity between each pair of objects in the feature space, training a multi-bit hash function using a greedy algorithm that increases the Hamming distance between dissimilar objects in the feature space while minimizing the Hamming distance between similar objects, and identifying semantic nearest neighbors for an object in a second feature space using the multi-bit hash function. A system embodiment includes a hash generator configured to generate the affinity matrix and train the multi-bit hash function, and a similarity determiner configured to identify semantic nearest neighbors for an object in a second feature space using the multi-bit hash function.

摘要翻译： 本文描述了用于识别特征空间中的语义最近邻居的方法，系统和制品。 方法实施例包括为给定特征空间中的对象生成亲和度矩阵，其中亲和矩阵识别特征空间中每对对象之间的语义相似性，使用增加汉明距离的贪心算法训练多比特哈希函数 在特征空间中的不相似对象之间，同时使相似对象之间的汉明距离最小化，并且使用多位哈希函数来识别第二特征空间中的对象的语义最近邻居。 系统实施例包括被配置为生成亲和度矩阵并训练多比特哈希函数的哈希发生器，以及被配置为使用多比特哈希函数来识别第二特征空间中的对象的语义最近邻居的相似性确定器。

公开(公告)号：US20200261290A1

公开(公告)日：2020-08-20

申请号：US15947099

申请日：2018-04-06

申请人： David Ross ,  Rositsa Stoykova

发明人： David Ross ,  Rositsa Stoykova

IPC分类号： A61G7/015 ,  A61G7/018

摘要： Embodiments are described for a articulating bed frame which may be utilized as a bed, a chair, or a lift chair. The articulating bed frame has a support frame with a mattress resting thereon. A plurality of support members is in operative communication with at least one motor. A controller allows the user to select from a plurality of configurations and thus modulate the orientation of the bed.