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公开(公告)号：US20150248167A1
公开(公告)日：2015-09-03
申请号：US14242649
申请日：2014-04-01
发明人： Henrik Turbell , Mattias Nilsson , Renat Vafin , Jekaterina Pinding , Antonio Criminisi , Indeera Munasinghe
IPC分类号： G06F3/01 , H04N7/15 , G06F3/0481
CPC分类号： G06F3/017 , G06F3/012 , G06F3/0304 , G06F3/0481 , G06F3/165 , H04N7/15
摘要： Methods and systems for controlling a computing-based device based on gestures made within a predetermined range of a camera wherein the predetermined range is a subset of the field of view of the camera. Any gestures made outside of the predetermined range are ignored and do not cause the computing-based device to perform any action. In some examples, the gestures are used to control a drawing canvas that is implemented in a video conference session. In these examples, a single camera may be used to generate an image of a video conference user which is used to detect gestures in the predetermined range and provide other parties to the video conference session a visual image of the user.
摘要翻译： 用于基于在相机的预定范围内进行的手势来控制基于计算的设备的方法和系统，其中所述预定范围是所述相机视场的子集。 超出预定范围的任何手势都将被忽略，不会导致基于计算的设备执行任何操作。 在一些示例中，手势用于控制在视频会议会话中实现的绘图画布。 在这些示例中，可以使用单个相机来生成视频会议用户的图像，该图像用于检测预定范围内的手势，并向视频会议会话的其他方提供用户的可视图像。
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公开(公告)号：US20140307956A1
公开(公告)日：2014-10-16
申请号：US13860515
申请日：2013-04-10
CPC分类号： G06K9/627 , G06K9/00536 , G06K9/00718 , G06K9/6215 , G06K9/6217 , G06K9/6219 , G06K9/6267 , G06K9/6281 , G06K9/6282 , G06K9/6292 , G06K9/72 , G06K2209/051
摘要： Image labeling is described, for example, to recognize body organs in a medical image, to label body parts in a depth image of a game player, to label objects in a video of a scene. In various embodiments an automated classifier uses geodesic features of an image, and optionally other types of features, to semantically segment an image. For example, the geodesic features relate to a distance between image elements, the distance taking into account information about image content between the image elements. In some examples the automated classifier is an entangled random decision forest in which data accumulated at earlier tree levels is used to make decisions at later tree levels. In some examples the automated classifier has auto-context by comprising two or more random decision forests. In various examples parallel processing and look up procedures are used.
摘要翻译： 图像标记例如被描述为识别医学图像中的身体器官，以在玩家的深度图像中标记身体部位来标记场景的视频中的对象。 在各种实施例中，自动分类器使用图像的测地学特征以及可选地其他类型的特征来语义地分割图像。 例如，测地特征与图像元素之间的距离相关，该距离考虑了关于图像元素之间的图像内容的信息。 在一些示例中，自动分类器是纠缠的随机决策树，其中在较早的树级积累的数据用于在稍后的树级做出决定。 在一些示例中，自动分类器具有包含两个或更多个随机决策树的自动上下文。 在各种示例中，使用并行处理和查找过程。
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公开(公告)号：US20160071284A1
公开(公告)日：2016-03-10
申请号：US14536660
申请日：2014-11-09
发明人： Peter Kontschieder , Jonas Dorn , Darko Zikic , Antonio Criminisi
IPC分类号： G06T7/20
CPC分类号： G06K9/00744 , G06F17/30784 , G06F17/30887 , G06F19/00 , G06F19/3481 , G06K9/00342 , G06K9/6267 , G06K9/6278 , G06K2009/00738 , G06N5/00 , G06N5/025 , G06N99/005 , G06T7/0012 , G06T7/20 , G06T2207/10016 , G06T2207/20081 , G16H50/20
摘要： Video processing for motor task analysis is described. In various examples, a video of at least part of a person or animal carrying out a motor task, such as placing the forefinger on the nose, is input to a trained machine learning system to classify the motor task into one of a plurality of classes. In an example, motion descriptors such as optical flow are computed from pairs of frames of the video and the motion descriptors are input to the machine learning system. For example, during training the machine learning system identifies time-dependent and/or location-dependent acceleration or velocity features which discriminate between the classes of the motor task. In examples, the trained machine learning system computes, from the motion descriptors, the location dependent acceleration or velocity features which it has learned as being good discriminators. In various examples, a feature is computed using sub-volumes of the video.
摘要翻译： 描述了电机任务分析的视频处理。 在各种示例中，执行运动任务的人或动物的至少一部分的视频，例如将食指放在鼻子上，被输入到经过训练的机器学习系统中，以将运动任务分类为多个等级之一 。 在一个示例中，诸如光流的运动描述符是根据视频的帧对计算的，运动描述符被输入到机器学习系统。 例如，在训练期间，机器学习系统识别区分马达任务类别的时间依赖和/或位置相关的加速度或速度特征。 在实例中，经过训练的机器学习系统从运动描述符计算其已被认为是良好鉴别器的位置相关加速度或速度特征。 在各种示例中，使用视频的子卷计算特征。
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公开(公告)号：US20140241617A1
公开(公告)日：2014-08-28
申请号：US13774145
申请日：2013-02-22
发明人： Jamie Daniel Joseph Shotton , Benjamin Michael Glocker , Christopher Zach , Shahram Izadi , Antonio Criminisi , Andrew William Fitzgibbon
IPC分类号： G06K9/66
CPC分类号： G06K9/66 , G06K9/00671 , G06K9/6219 , G06K9/6256 , G06K9/6282
摘要： Camera or object pose calculation is described, for example, to relocalize a mobile camera (such as on a smart phone) in a known environment or to compute the pose of an object moving relative to a fixed camera. The pose information is useful for robotics, augmented reality, navigation and other applications. In various embodiments where camera pose is calculated, a trained machine learning system associates image elements from an image of a scene, with points in the scene's 3D world coordinate frame. In examples where the camera is fixed and the pose of an object is to be calculated, the trained machine learning system associates image elements from an image of the object with points in an object coordinate frame. In examples, the image elements may be noisy and incomplete and a pose inference engine calculates an accurate estimate of the pose.
摘要翻译： 描述了摄像机或物体姿态计算，例如，在已知环境中重新定位移动摄像机（例如智能电话）或计算相对于固定摄像机移动的物体的姿态。 姿态信息对于机器人，增强现实，导航和其他应用是有用的。 在计算相机姿态的各种实施例中，经过训练的机器学习系统将来自场景的图像的图像元素与场景的3D世界坐标系中的点相关联。 在相机固定并且要计算对象的姿态的示例中，训练的机器学习系统将来自对象的图像的图像元素与对象坐标系中的点相关联。 在示例中，图像元素可能是噪声和不完整的，并且姿态推理引擎计算姿态的准确估计。
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公开(公告)号：US09280719B2
公开(公告)日：2016-03-08
申请号：US14148404
申请日：2014-01-06
发明人： Antonio Criminisi , Jamie Daniel Joseph Shotton , Andrew Fitzgibbon , Toby Sharp , Matthew Darius Cook
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。
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公开(公告)号：US20150248765A1
公开(公告)日：2015-09-03
申请号：US14193079
申请日：2014-02-28
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： 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.
摘要翻译： 使用RGB相机感测深度的方法。 在示例方法中，从RGB相机接收场景的彩色图像。 将彩色图像应用于训练有素的机器学习部件，其使用图像元素的特征来分配全部或部分图像元素，该深度值表示由图像元素和RGB相机所描绘的表面之间的距离。 在各种示例中，机器学习部件包括一个或多个缠结测地线随机决策树。
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公开(公告)号：US20150134576A1
公开(公告)日：2015-05-14
申请号：US14079394
申请日：2013-11-13
发明人： Jamie Daniel Joseph Shotton , Toby Sharp , Pushmeet Kohli , Reinhard Sebastian Bernhard Nowozin , John Michael Winn , Antonio Criminisi
IPC分类号： G06N99/00
CPC分类号： G06N99/005 , G06F17/30598 , G06F17/30958 , G06K9/00335 , G06K9/00369 , G06K9/6257 , G06K9/6269 , G06K9/6282 , G06N5/003 , G06N7/005
摘要： Memory facilitation using directed acyclic graphs is described, for example, where a plurality of directed acyclic graphs are trained for gesture recognition from human skeletal data, or to estimate human body joint positions from depth images for gesture detection. In various examples directed acyclic graphs are grown during training using a training objective which takes into account both connection patterns between nodes and split function parameter values. For example, a layer of child nodes is grown and connected to a parent layer of nodes using an initialization strategy. In examples, various local search processes are used to find good combinations of connection patterns and split function parameters.
摘要翻译： 描述使用有向非循环图的存储器便利化，例如，其中针对来自人类骨骼数据的手势识别训练多个有向非循环图，或者从用于手势检测的深度图像估计人体关节位置。 在各种示例中，使用训练目标在训练期间生长定向非循环图，其考虑了节点之间的连接模式和分离功能参数值。 例如，使用初始化策略生长子层节点并连接到父层节点。 在示例中，使用各种本地搜索过程来找到连接模式和分割功能参数的良好组合。
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公开(公告)号：US20150296152A1
公开(公告)日：2015-10-15
申请号：US14252638
申请日：2014-04-14
发明人： Sean Ryan Francesco Fanello , Cem Keskin , Pushmeet Kohli , Shahram Izadi , Jamie Daniel Joseph Shotton , Antonio Criminisi
CPC分类号： G06K9/52 , G06K9/0051 , G06K9/40 , G06K9/6268 , G06T5/002 , G06T5/20 , G06T2207/10028 , G06T2207/20081 , H04N5/217
摘要： Filtering sensor data is described, for example, where filters conditioned on a local appearance of the signal are predicted by a machine learning system, and used to filter the sensor data. In various examples the sensor data is a stream of noisy video image data and the filtering process denoises the video stream. In various examples the sensor data is a depth image and the filtering process refines the depth image which may then be used for gesture recognition or other purposes. In various examples the sensor data is one dimensional measurement data from an electric motor and the filtering process denoises the measurements. In examples the machine learning system comprises a random decision forest where trees of the forest store filters at their leaves. In examples, the random decision forest is trained using a training objective with a data dependent regularization term.
摘要翻译： 对过滤传感器数据进行描述，例如，其中通过机器学习系统预测基于信号的局部外观的滤波器，并且用于过滤传感器数据。 在各种示例中，传感器数据是噪声视频图像数据流，并且滤波处理去除视频流。 在各种示例中，传感器数据是深度图像，并且滤波处理优化深度图像，然后可以将其用于手势识别或其他目的。 在各种示例中，传感器数据是来自电动机的一维测量数据，并且滤波处理去除了测量结果。 在实例中，机器学习系统包括森林商店的树木在其叶子处过滤的随机决策树。 在实例中，使用具有数据相关正则化项的训练目标对随机决策树进行训练。
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公开(公告)号：US20140126821A1
公开(公告)日：2014-05-08
申请号：US14148404
申请日：2014-01-06
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。
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