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1. 发明授权

US08625897B2 Foreground and background image segmentation 有权
标题翻译：前景和背景图像分割
公开(公告)号：US08625897B2
公开(公告)日：2014-01-07
申请号：US12790026
申请日：2010-05-28
申请人： Antonio Criminisi , Jamie Daniel Joseph Shotton , Andrew Fitzgibbon , Toby Sharp , Matthew Darius Cook
发明人： Antonio Criminisi , Jamie Daniel Joseph Shotton , Andrew Fitzgibbon , Toby Sharp , Matthew Darius Cook
IPC分类号： G06K9/34
CPC分类号： G06K9/34 , G06K9/38 , G06T7/11 , G06T7/168 , G06T7/187 , G06T7/194 , G06T2207/10016 , G06T2207/20048 , G06T2207/20156 , G06T2207/30196 , H04N13/239
摘要： Foreground and background image segmentation is described. In an example, a seed region is selected in a foreground portion of an image, and a geodesic distance is calculated from each image element to the seed region. A subset of the image elements having a geodesic distance less than a threshold is determined, and this subset of image elements are labeled as foreground. In another example, an image element from an image showing at least a user, a foreground object in proximity to the user, and a background is applied to trained decision trees to obtain probabilities of the image element representing one of these items, and a corresponding classification assigned to the image element. This is repeated for each image element. Image elements classified as belonging to the user are labeled as foreground, and image elements classified as foreground objects or background are labeled as background.
摘要翻译：描述了前景和背景图像分割。在一个示例中，在图像的前景部分中选择种子区域，并且从每个图像元素计算到种子区域的测地距离。确定具有小于阈值的测地距离的图像元素的子集，并且该图像元素的子集被标记为前景。在另一示例中，将来自显示至少用户的图像，邻近用户的前景对象和背景的图像元素应用于经过训练的决策树，以获得表示这些项目之一的图像元素的概率，以及相应的分类到图像元素的分类。对于每个图像元素重复这一点。分类为属于用户的图像元素被标记为前景，并且被分类为前景对象或背景的图像元素被标记为背景。

2. 发明申请

US20110293180A1 Foreground and Background Image Segmentation 有权
标题翻译：前景和背景图像分割
公开(公告)号：US20110293180A1
公开(公告)日：2011-12-01
申请号：US12790026
申请日：2010-05-28
申请人： Antonio Criminisi , Jamie Daniel Joseph Shotton , Andrew Fitzgibbon , Toby Sharp , Matthew Darius Cook
发明人： Antonio Criminisi , Jamie Daniel Joseph Shotton , Andrew Fitzgibbon , Toby Sharp , Matthew Darius Cook
IPC分类号： G06K9/34 , H04N13/02
CPC分类号： G06K9/34 , G06K9/38 , G06T7/11 , G06T7/168 , G06T7/187 , G06T7/194 , G06T2207/10016 , G06T2207/20048 , G06T2207/20156 , G06T2207/30196 , H04N13/239
摘要： Foreground and background image segmentation is described. In an example, a seed region is selected in a foreground portion of an image, and a geodesic distance is calculated from each image element to the seed region. A subset of the image elements having a geodesic distance less than a threshold is determined, and this subset of image elements are labeled as foreground. In another example, an image element from an image showing at least a user, a foreground object in proximity to the user, and a background is applied to trained decision trees to obtain probabilities of the image element representing one of these items, and a corresponding classification assigned to the image element. This is repeated for each image element. Image elements classified as belonging to the user are labeled as foreground, and image elements classified as foreground objects or background are labeled as background.
摘要翻译：描述了前景和背景图像分割。在一个示例中，在图像的前景部分中选择种子区域，并且从每个图像元素计算到种子区域的测地距离。确定具有小于阈值的测地距离的图像元素的子集，并且该图像元素的子集被标记为前景。在另一示例中，将来自显示至少用户的图像，邻近用户的前景对象和背景的图像元素应用于经过训练的决策树，以获得表示这些项目之一的图像元素的概率，以及相应的分类到图像元素的分类。对于每个图像元素重复这一点。分类为属于用户的图像元素被标记为前景，并且被分类为前景对象或背景的图像元素被标记为背景。

3. 发明授权

US08638985B2 Human body pose estimation 有权
标题翻译：人体姿势估计
公开(公告)号：US08638985B2
公开(公告)日：2014-01-28
申请号：US13040205
申请日：2011-03-03
申请人： Jamie Daniel Joseph Shotton , Shahram Izadi , Otmar Hilliges , David Kim , David Geoffrey Molyneaux , Matthew Darius Cook , Pushmeet Kohli , Antonio Criminisi , Ross Brook Girshick , Andrew William Fitzgibbon
发明人： Jamie Daniel Joseph Shotton , Shahram Izadi , Otmar Hilliges , David Kim , David Geoffrey Molyneaux , Matthew Darius Cook , Pushmeet Kohli , Antonio Criminisi , Ross Brook Girshick , Andrew William Fitzgibbon
IPC分类号： G06K9/00
CPC分类号： G06K9/00369
摘要： Techniques for human body pose estimation are disclosed herein. Images such as depth images, silhouette images, or volumetric images may be generated and pixels or voxels of the images may be identified. The techniques may process the pixels or voxels to determine a probability that each pixel or voxel is associated with a segment of a body captured in the image or to determine a three-dimensional representation for each pixel or voxel that is associated with a location on a canonical body. These probabilities or three-dimensional representations may then be utilized along with the images to construct a posed model of the body captured in the image.
摘要翻译：本文公开了人体姿势估计技术。可以生成诸如深度图像，剪影图像或体积图像的图像，并且可以识别图像的像素或体素。这些技术可以处理像素或体素以确定每个像素或体素与在图像中捕获的身体的片段相关联的概率，或者确定与每个像素或体素上的位置相关联的每个像素或体素的三维表示规范身体。然后可以将这些概率或三维表示与图像一起使用以构建在图像中捕获的身体的姿态模型。

4. 发明申请

US20110210915A1 Human Body Pose Estimation 有权
标题翻译：人体姿势估计
公开(公告)号：US20110210915A1
公开(公告)日：2011-09-01
申请号：US13040205
申请日：2011-03-03
申请人： Jamie Daniel Joseph Shotton , Shahram Izadi , Otmar Hilliges , David Kim , David Geoffrey Molyneaux , Matthew Darius Cook , Pushmeet Kohli , Antonio Criminisi , Ross Brook Girshick , Andrew William Fitzgibbon
发明人： Jamie Daniel Joseph Shotton , Shahram Izadi , Otmar Hilliges , David Kim , David Geoffrey Molyneaux , Matthew Darius Cook , Pushmeet Kohli , Antonio Criminisi , Ross Brook Girshick , Andrew William Fitzgibbon
IPC分类号： G06K9/00 , G06F3/033
CPC分类号： G06K9/00369
摘要： Techniques for human body pose estimation are disclosed herein. Images such as depth images, silhouette images, or volumetric images may be generated and pixels or voxels of the images may be identified. The techniques may process the pixels or voxels to determine a probability that each pixel or voxel is associated with a segment of a body captured in the image or to determine a three-dimensional representation for each pixel or voxel that is associated with a location on a canonical body. These probabilities or three-dimensional representations may then be utilized along with the images to construct a posed model of the body captured in the image.
摘要翻译：本文公开了人体姿势估计技术。可以生成诸如深度图像，剪影图像或体积图像的图像，并且可以识别图像的像素或体素。这些技术可以处理像素或体素以确定每个像素或体素与在图像中捕获的身体的片段相关联的概率，或者确定与每个像素或体素上的位置相关联的每个像素或体素的三维表示规范身体。然后可以将这些概率或三维表示与图像一起使用以构建在图像中捕获的身体的姿态模型。

5. 发明授权

US08571263B2 Predicting joint positions 有权
标题翻译：预测联合职位
公开(公告)号：US08571263B2
公开(公告)日：2013-10-29
申请号：US13050858
申请日：2011-03-17
申请人： Jamie Daniel Joseph Shotton , Pushmeet Kohli , Ross Brook Girshick , Andrew Fitzgibbon , Antonio Criminisi
发明人： Jamie Daniel Joseph Shotton , Pushmeet Kohli , Ross Brook Girshick , Andrew Fitzgibbon , Antonio Criminisi
IPC分类号： G06K9/00
CPC分类号： G06F3/017 , G06K9/00362 , G06N5/025
摘要： Predicting joint positions is described, for example, to find joint positions of humans or animals (or parts thereof) in an image to control a computer game or for other applications. In an embodiment image elements of a depth image make joint position votes so that for example, an image element depicting part of a torso may vote for a position of a neck joint, a left knee joint and a right knee joint. A random decision forest may be trained to enable image elements to vote for the positions of one or more joints and the training process may use training images of bodies with specified joint positions. In an example a joint position vote is expressed as a vector representing a distance and a direction of a joint position from an image element making the vote. The random decision forest may be trained using a mixture of objectives.
摘要翻译：例如，描述关节位置的描述是为了在图像中找到人或动物（或其部分）的联合位置，以控制计算机游戏或用于其他应用。在一个实施例中，深度图像的图像元素进行联合位置投票，使得例如描绘躯干的一部分的图像元素可以投射颈部关节，左膝关节和右膝关节的位置。可以对随机决策林进行训练，以使图像元素能够对一个或多个关节的位置进行投票，并且训练过程可以使用具有指定关节位置的身体的训练图像。在一个例子中，联合立场表决被表示为表示从投票的图像元素的联合位置的距离和方向的向量。可以使用目标混合来训练随机决策林。

6. 发明申请

US20120239174A1 Predicting Joint Positions 有权
标题翻译：预测联合位置
公开(公告)号：US20120239174A1
公开(公告)日：2012-09-20
申请号：US13050858
申请日：2011-03-17
申请人： Jamie Daniel Joseph Shotton , Pushmeet Kohli , Ross Brook Girshick , Andrew Fitzgibbon , Antonio Criminisi
发明人： Jamie Daniel Joseph Shotton , Pushmeet Kohli , Ross Brook Girshick , Andrew Fitzgibbon , Antonio Criminisi
IPC分类号： G06F19/00 , G06K9/62 , G06K9/68
CPC分类号： G06F3/017 , G06K9/00362 , G06N5/025
摘要： Predicting joint positions is described, for example, to find joint positions of humans or animals (or parts thereof) in an image to control a computer game or for other applications. In an embodiment image elements of a depth image make joint position votes so that for example, an image element depicting part of a torso may vote for a position of a neck joint, a left knee joint and a right knee joint. A random decision forest may be trained to enable image elements to vote for the positions of one or more joints and the training process may use training images of bodies with specified joint positions. In an example a joint position vote is expressed as a vector representing a distance and a direction of a joint position from an image element making the vote. The random decision forest may be trained using a mixture of objectives.
摘要翻译：例如，描述关节位置的描述是为了在图像中找到人或动物（或其部分）的联合位置，以控制计算机游戏或用于其他应用。在一个实施例中，深度图像的图像元素进行联合位置投票，使得例如描绘躯干的一部分的图像元素可以投射颈部关节，左膝关节和右膝关节的位置。可以对随机决策林进行训练，以使图像元素能够对一个或多个关节的位置进行投票，并且训练过程可以使用具有指定关节位置的身体的训练图像。在一个例子中，联合立场表决被表示为表示从投票的图像元素的联合位置的距离和方向的向量。可以使用目标混合来训练随机决策林。

7. 发明申请

US20130129230A1 Computing Pose and/or Shape of Modifiable Entities 有权
标题翻译：可修改实体的计算姿势和/或形状
公开(公告)号：US20130129230A1
公开(公告)日：2013-05-23
申请号：US13300542
申请日：2011-11-18
申请人： Jamie Daniel Joseph Shotton , Andrew William Fitzgibbon , Jonathan James Taylor , Matthew Darius Cook
发明人： Jamie Daniel Joseph Shotton , Andrew William Fitzgibbon , Jonathan James Taylor , Matthew Darius Cook
IPC分类号： G06K9/68
CPC分类号： G06K9/00214 , G06T7/75 , G06T17/00
摘要： Computing pose and/or shape of a modifiable entity is described. In various embodiments a model of an entity (such as a human hand, a golf player holding a golf club, an animal, a body organ) is fitted to an image depicting an example of the entity in a particular pose and shape. In examples, an optimization process finds values of pose and/or shape parameters that when applied to the model explain the image data well. In examples the optimization process is influenced by correspondences between image elements and model points obtained from a regression engine where the regression engine may be a random decision forest. For example, the random decision forest may take elements of the image and calculate candidate correspondences between those image elements and model points. In examples the model, pose and correspondences may be used for control of various applications including computer games, medical systems, augmented reality.
摘要翻译：描述可修改实体的计算姿势和/或形状。在各种实施例中，将实体（诸如人的手，持有高尔夫球杆，高尔夫球杆，动物，身体器官的高尔夫球手）的模型安装在描绘特定姿势和形状的实体的示例的图像上。在示例中，优化过程找到姿态和/或形状参数的值，当应用于模型时，可以很好地解释图像数据。在示例中，优化过程受图像元素和从回归引擎获得的模型点之间的对应性的影响，回归引擎可以是随机决策树。例如，随机决策树可以采用图像的元素，并计算这些图像元素和模型点之间的候选对应关系。在示例中，模型，姿态和对应可以用于控制各种应用，包括计算机游戏，医疗系统，增强现实。

8. 发明授权

US08724906B2 Computing pose and/or shape of modifiable entities 有权
标题翻译：计算可修改实体的姿态和/或形状
公开(公告)号：US08724906B2
公开(公告)日：2014-05-13
申请号：US13300542
申请日：2011-11-18
申请人： Jamie Daniel Joseph Shotton , Andrew William Fitzgibbon , Jonathan James Taylor , Matthew Darius Cook
发明人： Jamie Daniel Joseph Shotton , Andrew William Fitzgibbon , Jonathan James Taylor , Matthew Darius Cook
IPC分类号： G06K9/68 , G06K9/62
CPC分类号： G06K9/00214 , G06T7/75 , G06T17/00
摘要： Computing pose and/or shape of a modifiable entity is described. In various embodiments a model of an entity (such as a human hand, a golf player holding a golf club, an animal, a body organ) is fitted to an image depicting an example of the entity in a particular pose and shape. In examples, an optimization process finds values of pose and/or shape parameters that when applied to the model explain the image data well. In examples the optimization process is influenced by correspondences between image elements and model points obtained from a regression engine where the regression engine may be a random decision forest. For example, the random decision forest may take elements of the image and calculate candidate correspondences between those image elements and model points. In examples the model, pose and correspondences may be used for control of various applications including computer games, medical systems, augmented reality.
摘要翻译：描述可修改实体的计算姿势和/或形状。在各种实施例中，将实体（诸如人的手，持有高尔夫球杆，高尔夫球杆，动物，身体器官的高尔夫球手）的模型安装在描绘特定姿势和形状的实体的示例的图像上。在示例中，优化过程找到姿态和/或形状参数的值，当应用于模型时，可以很好地解释图像数据。在示例中，优化过程受图像元素和从回归引擎获得的模型点之间的对应性的影响，回归引擎可以是随机决策树。例如，随机决策树可以采用图像的元素，并计算这些图像元素和模型点之间的候选对应关系。在示例中，模型，姿态和对应可以用于控制各种应用，包括计算机游戏，医疗系统，增强现实。

9. 发明授权

US09626766B2 Depth sensing using an RGB camera 有权
公开(公告)号：US09626766B2
公开(公告)日：2017-04-18
申请号：US14193079
申请日：2014-02-28
申请人： Antonio Criminisi , Duncan Paul Robertson , Peter Kontschieder , Pushmeet Kohli , Henrik Turbell , Adriana Dumitras , Indeera Munasinghe , Jamie Daniel Joseph Shotton
发明人： Antonio Criminisi , Duncan Paul Robertson , Peter Kontschieder , Pushmeet Kohli , Henrik Turbell , Adriana Dumitras , Indeera Munasinghe , Jamie Daniel Joseph Shotton
IPC分类号： G06K9/00 , G06T7/00
CPC分类号： G06T7/50 , G06T2207/10016 , G06T2207/10024 , G06T2207/20072 , G06T2207/20081
摘要： A method of sensing depth using an RGB camera. In an example method, a color image of a scene is received from an RGB camera. The color image is applied to a trained machine learning component which uses features of the image elements to assign all or some of the image elements a depth value which represents the distance between the surface depicted by the image element and the RGB camera. In various examples, the machine learning component comprises one or more entangled geodesic random decision forests.

10. 发明申请

US20130346346A1 SEMI-SUPERVISED RANDOM DECISION FORESTS FOR MACHINE LEARNING 有权
标题翻译：半自动监控机器学习的随机决策林
公开(公告)号：US20130346346A1
公开(公告)日：2013-12-26
申请号：US13528876
申请日：2012-06-21
申请人： Antonio Criminisi , Jamie Daniel Joseph Shotton
发明人： Antonio Criminisi , Jamie Daniel Joseph Shotton
IPC分类号： G06F15/18
CPC分类号： G06N99/005 , G06N5/02 , G06N7/005
摘要： Semi-supervised random decision forests for machine learning are described, for example, for interactive image segmentation, medical image analysis, and many other applications. In examples, a random decision forest comprising a plurality of hierarchical data structures is trained using both unlabeled and labeled observations. In examples, a training objective is used which seeks to cluster the observations based on the labels and similarity of the observations. In an example, a transducer assigns labels to the unlabeled observations on the basis of the clusters and certainty information. In an example, an inducer forms a generic clustering function by counting examples of class labels at leaves of the trees in the forest. In an example, an active learning module identifies regions in a feature space from which the observations are drawn using the clusters and certainty information; new observations from the identified regions are used to train the random decision forest.
摘要翻译：描述了用于机器学习的半监督随机决策树，例如用于交互式图像分割，医学图像分析和许多其他应用。在示例中，使用未标记和标记的观察来训练包括多个分级数据结构的随机决策林。在实例中，使用了一个训练目标，其目的是根据观察结果的标签和相似性对观测进行聚类。在一个示例中，传感器基于集群和确定性信息将标签分配给未标记的观察。在一个例子中，诱导者通过计算森林中树的树叶上的类标签的示例来形成通用聚类函数。在一个示例中，主动学习模块识别特征空间中的区域，使用聚类和确定性信息从中绘制观察值; 来自确定地区的新观察用于训练随机决策林。

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