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    Three-Dimensional Environment Reconstruction
    22.
    发明申请
    Three-Dimensional Environment Reconstruction 有权
    三维环境重建

    公开(公告)号：US20120194516A1

    公开(公告)日：2012-08-02

    申请号：US13017690

    申请日：2011-01-31

    申请人： Richard Newcombe ,  Shahram Izadi ,  David Molyneaux ,  Otmar Hilliges ,  David Kim ,  Jamie Daniel Joseph Shotton ,  Stephen Edward Hodges ,  David Alexander Butler ,  Andrew Fitzgibbon ,  Pushmeet Kohli

    发明人： Richard Newcombe ,  Shahram Izadi ,  David Molyneaux ,  Otmar Hilliges ,  David Kim ,  Jamie Daniel Joseph Shotton ,  Stephen Edward Hodges ,  David Alexander Butler ,  Andrew Fitzgibbon ,  Pushmeet Kohli

    IPC分类号： G06T17/00

    CPC分类号： G06T17/00 ,  G06T2200/08

    摘要： Three-dimensional environment reconstruction is described. In an example, a 3D model of a real-world environment is generated in a 3D volume made up of voxels stored on a memory device. The model is built from data describing a camera location and orientation, and a depth image with pixels indicating a distance from the camera to a point in the environment. A separate execution thread is assigned to each voxel in a plane of the volume. Each thread uses the camera location and orientation to determine a corresponding depth image location for its associated voxel, determines a factor relating to the distance between the associated voxel and the point in the environment at the corresponding location, and updates a stored value at the associated voxel using the factor. Each thread iterates through an equivalent voxel in the remaining planes of the volume, repeating the process to update the stored value.

    摘要翻译： 描述了三维环境重建。 在一个示例中，在由存储在存储器件上的体素组成的3D体积中生成真实世界环境的3D模型。 该模型是从描述相机位置和方向的数据构建的，以及具有指示从相机到环境中的点的距离的像素的深度图像。 单独的执行线程被分配给卷的平面中的每个体素。 每个线程使用摄像机位置和方向来确定其相关体素的相应深度图像位置，确定与相关体素和相应位置处的环境中的点之间的距离有关的因子，并更新相关联的体素的存储值 体素使用因素。 每个线程遍历卷的剩余平面中的等效体素，重复更新存储值的过程。

    Automatic Identification of Image Features
    23.
    发明申请
    Automatic Identification of Image Features 审中-公开
    图像特征的自动识别

    公开(公告)号：US20110188715A1

    公开(公告)日：2011-08-04

    申请号：US12697785

    申请日：2010-02-01

    申请人： Jamie Daniel Joseph Shotton ,  Antonio Criminisi

    发明人： Jamie Daniel Joseph Shotton ,  Antonio Criminisi

    IPC分类号： G06K9/00

    CPC分类号： G06K9/00 ,  G06K9/6282 ,  G06K2209/051 ,  G06T7/0012

    摘要： Automatic identification of image features is described. In an embodiment, a device automatically identifies organs in a medical image using a decision forest formed of a plurality of distinct, trained decision trees. An image element from the image is applied to each of the trained decision trees to obtain a probability of the image element representing a predefined class of organ. The probabilities from each of the decision trees are aggregated and used to assign an organ classification to the image element. In another embodiment, a method of training a decision tree to identify features in an image is provided. For a selected node in the decision tree, a training image is analyzed at a plurality of locations offset from a selected image element, and one of the offsets is selected based on the results of the analysis and stored in association with the node.

    摘要翻译： 描述图像特征的自动识别。 在一个实施例中，设备使用由多个不同的训练有素的决策树形成的决策树，自动识别医学图像中的器官。 将来自图像的图像元素应用于每个经训练的决策树，以获得表示预定类别器官的图像元素的概率。 来自每个决策树的概率被聚合并用于将分类器官分类给图像元素。 在另一个实施例中，提供了一种训练决策树以识别图像中的特征的方法。 对于决策树中的选定节点，在与所选择的图像元素偏移的多个位置处分析训练图像，并且基于分析的结果来选择偏移中的一个，并且与节点相关联地存储。

    Data Processing Using Restricted Boltzmann Machines
    24.
    发明申请
    Data Processing Using Restricted Boltzmann Machines 有权
    使用限制玻尔兹曼机器的数据处理

    公开(公告)号：US20100228694A1

    公开(公告)日：2010-09-09

    申请号：US12400388

    申请日：2009-03-09

    申请人： Nicolas Le Roux ,  John Winn ,  Jamie Daniel Joseph Shotton

    发明人： Nicolas Le Roux ,  John Winn ,  Jamie Daniel Joseph Shotton

    IPC分类号： G06N3/08

    CPC分类号： G06N3/08

    摘要： Data processing using restricted Boltzmann machines is described, for example, to pre-process continuous data and provide binary outputs. In embodiments, restricted Boltzmann machines based on either Gaussian distributions or Beta distributions are described which are able to learn and model both the mean and variance of data. In some embodiments, a stack of restricted Boltzmann machines are connected in series with outputs of one restricted Boltzmann machine providing input to the next in the stack and so on. Embodiments describe how training for each machine in the stack may be carried out efficiently and the combined system used for one of a variety of applications such as data compression, object recognition, image processing, information retrieval, data analysis and the like.

    摘要翻译： 例如，使用限制玻尔兹曼机器的数据处理被描述为预处理连续数据并提供二进制输出。 在实施例中，描述了基于高斯分布或Beta分布的限制Boltzmann机器，其能够学习和模拟数据的均值和方差。 在一些实施例中，一组受限制的波尔兹曼机器与一个限制波尔兹曼机器的输出串联连接，从而向堆叠中的下一个提供输入等等。 实施例描述了如何有效地执行堆栈中的每个机器的训练，以及用于诸如数据压缩，对象识别，图像处理，信息检索，数据分析等的各种应用之一的组合系统。

    Depth image compression
    27.
    发明授权
    Depth image compression 有权
    深度图像压缩

    公开(公告)号：US09557836B2

    公开(公告)日：2017-01-31

    申请号：US13286966

    申请日：2011-11-01

    申请人： Toby Sharp ,  Jamie Daniel Joseph Shotton

    发明人： Toby Sharp ,  Jamie Daniel Joseph Shotton

    IPC分类号： G06K9/00 ,  G06K9/62 ,  G06T7/00 ,  G06F3/038 ,  A63F13/42 ,  G06F3/01 ,  G06F3/03 ,  H04N19/597

    CPC分类号： G06F3/038 ,  A63F13/42 ,  A63F2300/1093 ,  A63F2300/6045 ,  G06F3/017 ,  G06F3/0304 ,  G06K9/00335 ,  G06K9/00362 ,  G06K9/00369 ,  G06K9/00375 ,  G06K9/6269 ,  G06T2207/10028 ,  G06T2207/30196 ,  H04N19/597

    摘要： Depth image compression is described for example, to enable body-part centers of players of a game to be detected in real time from depth images or for other applications such as augmented reality, and human-computer interaction. In an embodiment, depth images which have associated body-part probabilities, are compressed using probability mass which is related to the depth of an image element and a probability of a body part for the image element. In various examples, compression of the depth images using probability mass enables body part center detection, by clustering output elements, to be speeded up. In some examples, the scale of the compression is selected according to a depth of a foreground region and in some cases different scales are used for different image regions. In some examples, certainties of the body-part centers are calculated using probability masses of clustered image elements.

    摘要翻译： 例如，深度图像压缩被描述为使得能够从深度图像或诸如增强现实和人机交互的其他应用实时地检测游戏的玩家的身体部位中心。 在一个实施例中，具有相关联的身体部位概率的深度图像使用与图像元素的深度和图像元素的身体部位的概率相关的概率质量进行压缩。 在各种示例中，使用概率质量压缩深度图像可以通过聚类输出元素来加快身体部位中心检测。 在一些示例中，根据前景区域的深度选择压缩的比例，并且在一些情况下，不同的比例尺用于不同的图像区域。 在一些示例中，使用聚类图像元素的概率质量来计算身体部位中心的确定性。

    Generating computer models of 3D objects
    28.
    发明授权
    Generating computer models of 3D objects 有权
    生成3D对象的计算机模型

    公开(公告)号：US09053571B2

    公开(公告)日：2015-06-09

    申请号：US13154288

    申请日：2011-06-06

    申请人： Jamie Daniel Joseph Shotton ,  Shahram Izadi ,  Otmar Hilliges ,  David Kim ,  David Molyneaux ,  Pushmeet Kohli ,  Andrew Fitzgibbon ,  Stephen Edward Hodges

    发明人： Jamie Daniel Joseph Shotton ,  Shahram Izadi ,  Otmar Hilliges ,  David Kim ,  David Molyneaux ,  Pushmeet Kohli ,  Andrew Fitzgibbon ,  Stephen Edward Hodges

    IPC分类号： G06T17/00 ,  G06T15/00 ,  G06T17/10 ,  G06T7/20

    CPC分类号： G06T7/251 ,  G06T17/10 ,  G06T2200/08 ,  G06T2207/10028

    摘要： Generating computer models of 3D objects is described. In one example, depth images of an object captured by a substantially static depth camera are used to generate the model, which is stored in a memory device in a three-dimensional volume. Portions of the depth image determined to relate to the background are removed to leave a foreground depth image. The position and orientation of the object in the foreground depth image is tracked by comparison to a preceding depth image, and the foreground depth image is integrated into the volume by using the position and orientation to determine where to add data derived from the foreground depth image into the volume. In examples, the object is hand-rotated by a user before the depth camera. Hands that occlude the object are integrated out of the model as they do not move in sync with the object due to re-gripping.

    摘要翻译： 描述生成3D对象的计算机模型。 在一个示例中，使用由基本上静态的深度相机拍摄的对象的深度图像来生成存储在三维体积中的存储器设备中的模型。 确定与背景相关的深度图像的部分被去除以留下前景深度图像。 通过与前一个深度图像进行比较来跟踪前景深度图像中的对象的位置和方向，并且通过使用位置和方向来将前景深度图像集成到卷中，以确定在哪里添加从前景深度图像导出的数据 进入卷。 在示例中，该对象在深度相机之前由用户手动旋转。 闭合对象的手从模型中集成出来，因为它们不会因为重新抓取而与对象同步移动。

    SEMI-SUPERVISED RANDOM DECISION FORESTS FOR MACHINE LEARNING
    29.
    发明申请
    SEMI-SUPERVISED RANDOM DECISION FORESTS FOR MACHINE LEARNING 有权
    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。