公开(公告)号：US20080178087A1

公开(公告)日：2008-07-24

申请号：US11625049

申请日：2007-01-19

Applicant: Andrew Fitzgibbon ,  Toby Sharp

Inventor： Andrew Fitzgibbon ,  Toby Sharp

IPC: G11B27/00 ,  G06F3/00

CPC classification number: G06T13/20 ,  G06T19/20 ,  G06T2219/2016

Abstract: Using in-scene editing, an added title, or object, moves as the camera moves through the imaged scene. Previously this has been complex to achieve, requiring expert users to explicitly align 3D coordinate systems in the image sequence and on the added title or object. For example, this has been used to add 3D objects into live-action footage in big-budget movies or advertising. A simple, easy to use system is described for achieving in-scene editing. A user specifies projection constraints by making 2D actions on one or more images in the image sequence. A 3D motion trajectory is computed for a 3D object model on the basis of the specified projection constraints and a smoothness indicator. Using the computed trajectory the 3D object model is added to the image sequence. Projection constraints may be added, amended or deleted to position the 3D object model and/or to animate it.

Abstract translation: 使用场景编辑，添加的标题或对象，随着相机移动通过成像的场景而移动。 以前，这一点很复杂，要求专家用户明确地对齐图像序列中的3D坐标系和添加的标题或对象。 例如，这已被用于将3D对象添加到大型预算电影或广告中的实时影像中。 描述了一个简单易用的系统，用于实现现场编辑。 用户通过在图像序列中的一个或多个图像上进行2D动作来指定投影约束。 基于指定的投影约束和平滑指标，为3D对象模型计算3D运动轨迹。 使用计算的轨迹，将3D对象模型添加到图像序列中。 可以添加，修改或删除投影约束以定位3D对象模型和/或使其动画化。

公开(公告)号：US08625897B2

公开(公告)日：2014-01-07

申请号：US12790026

申请日：2010-05-28

Applicant: Antonio Criminisi ,  Jamie Daniel Joseph Shotton ,  Andrew Fitzgibbon ,  Toby Sharp ,  Matthew Darius Cook

Inventor： Antonio Criminisi ,  Jamie Daniel Joseph Shotton ,  Andrew Fitzgibbon ,  Toby Sharp ,  Matthew Darius Cook

IPC: G06K9/34

CPC classification number: G06K9/34 ,  G06K9/38 ,  G06T7/11 ,  G06T7/168 ,  G06T7/187 ,  G06T7/194 ,  G06T2207/10016 ,  G06T2207/20048 ,  G06T2207/20156 ,  G06T2207/30196 ,  H04N13/239

Abstract: 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.

Abstract translation: 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。

公开(公告)号：US20110293180A1

公开(公告)日：2011-12-01

申请号：US12790026

申请日：2010-05-28

Applicant: Antonio Criminisi ,  Jamie Daniel Joseph Shotton ,  Andrew Fitzgibbon ,  Toby Sharp ,  Matthew Darius Cook

Inventor： Antonio Criminisi ,  Jamie Daniel Joseph Shotton ,  Andrew Fitzgibbon ,  Toby Sharp ,  Matthew Darius Cook

IPC: G06K9/34 ,  H04N13/02

CPC classification number: G06K9/34 ,  G06K9/38 ,  G06T7/11 ,  G06T7/168 ,  G06T7/187 ,  G06T7/194 ,  G06T2207/10016 ,  G06T2207/20048 ,  G06T2207/20156 ,  G06T2207/30196 ,  H04N13/239

Abstract: 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.

Abstract translation: 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。

公开(公告)号：US09053571B2

公开(公告)日：2015-06-09

申请号：US13154288

申请日：2011-06-06

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

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

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

CPC classification number: G06T7/251 ,  G06T17/10 ,  G06T2200/08 ,  G06T2207/10028

Abstract: 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.

Abstract translation: 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。

公开(公告)号：US08570320B2

公开(公告)日：2013-10-29

申请号：US13017729

申请日：2011-01-31

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

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

IPC: G06T17/00 ,  G09G5/00

CPC classification number: G06F3/011 ,  A63F13/65 ,  A63F2300/1087 ,  A63F2300/6009 ,  G06T7/20 ,  G06T17/00 ,  G06T2207/10016 ,  G06T2207/10028 ,  G06T2207/30244

Abstract: Use of a 3D environment model in gameplay is described. In an embodiment, a mobile depth camera is used to capture a series of depth images as it is moved around and a dense 3D model of the environment is generated from this series of depth images. This dense 3D model is incorporated within an interactive application, such as a game. The mobile depth camera is then placed in a static position for an interactive phase, which in some examples is gameplay, and the system detects motion of a user within a part of the environment from a second series of depth images captured by the camera. This motion provides a user input to the interactive application, such as a game. In further embodiments, automatic recognition and identification of objects within the 3D model may be performed and these identified objects then change the way that the interactive application operates.

Abstract translation: 描述了在游戏中使用3D环境模型。 在一个实施例中，移动深度相机被用来捕获一系列深度图像，因为它被移动，并且从该系列深度图像生成环境的密集3D模型。 这种密集的3D模型被并入在诸如游戏的交互式应用中。 然后将移动深度相机放置在用于交互式相位的静态位置，在一些示例中为游戏画面，并且系统从相机拍摄的第二系列深度图像中检测环境部分内的用户的运动。 该运动向诸如游戏的交互式应用提供用户输入。 在另外的实施例中，可以执行3D模型内的对象的自动识别和识别，并且这些识别的对象然后改变交互式应用程序的操作方式。

公开(公告)号：US08346800B2

公开(公告)日：2013-01-01

申请号：US12417511

申请日：2009-04-02

Applicant: Martin Szummer ,  Andrew Fitzgibbon ,  Lorenzo Torresani

Inventor： Martin Szummer ,  Andrew Fitzgibbon ,  Lorenzo Torresani

IPC: G06F17/30

CPC classification number: G06K9/6257 ,  G06F17/30705 ,  G06F17/3071

Abstract: Content-based information retrieval is described. In an example, a query item such as an image, document, email or other item is presented and items with similar content are retrieved from a database of items. In an example, each time a query is presented, a classifier is formed based on that query and using a training set of items. For example, the classifier is formed in real-time and is formed in such a way that a limit on the proportion of the items in the database that will be retrieved is set. In an embodiment, the query item is analyzed to identify tokens in that item and subsets of those tokens are selected to form the classifier. For example, the subsets of tokens are combined using Boolean operators in a manner which is efficient for searching on particular types of database.

Abstract translation: 描述基于内容的信息检索。 在一个示例中，呈现诸如图像，文档，电子邮件或其他项目的查询项目，并且从项目的数据库检索具有相似内容的项目。 在一个示例中，每次呈现查询时，基于该查询并使用项目的训练集形成分类器。 例如，分类器是实时形成的，并且以这样的方式形成：设置将要检索的数据库中的项目的比例的限制。 在一个实施例中，分析查询项目以识别该项目中的令牌，并且选择那些令牌的子集以形成分类器。 例如，使用布尔运算符组合令牌的子集，其方法对于在特定类型的数据库上进行搜索是有效的。

公开(公告)号：US20120194644A1

公开(公告)日：2012-08-02

申请号：US13017474

申请日：2011-01-31

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

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

IPC: H04N5/225

CPC classification number: G06T7/20 ,  G06T7/74 ,  G06T2207/10016 ,  G06T2207/10021 ,  G06T2207/10024 ,  G06T2207/10028 ,  G06T2207/30244

Abstract: Mobile camera localization using depth maps is described for robotics, immersive gaming, augmented reality and other applications. In an embodiment a mobile depth camera is tracked in an environment at the same time as a 3D model of the environment is formed using the sensed depth data. In an embodiment, when camera tracking fails, this is detected and the camera is relocalized either by using previously gathered keyframes or in other ways. In an embodiment, loop closures are detected in which the mobile camera revisits a location, by comparing features of a current depth map with the 3D model in real time. In embodiments the detected loop closures are used to improve the consistency and accuracy of the 3D model of the environment.

Abstract translation: 使用深度图的移动摄像机定位被描述为机器人，沉浸式游戏，增强现实和其他应用。 在一个实施例中，移动深度相机在环境中跟踪，同时使用感测的深度数据形成环境的3D模型。 在一个实施例中，当相机跟踪失败时，检测到该相机并且通过使用先前收集的关键帧或以其它方式来重新定位相机。 在一个实施例中，通过将当前深度图与3D模型的特征实时比较，检测到环路闭合，其中移动摄像机重新访问位置。 在实施例中，检测到的环路闭合用于改善环境的3D模型的一致性和准确性。

公开(公告)号：US20090080036A1

公开(公告)日：2009-03-26

申请号：US12299349

申请日：2007-05-03

Applicant: James Paterson ,  Ronald William Daniel ,  David Claus ,  Andrew Fitzgibbon

Inventor： James Paterson ,  Ronald William Daniel ,  David Claus ,  Andrew Fitzgibbon

IPC: H04N1/04

CPC classification number: G01B11/2518 ,  G06K9/2036 ,  G06K9/4661

Abstract: A scanner system and corresponding method, the system comprising: a scanner device (1); a target 17) and a processor (21). The scanner device (1) comprises: an emitter (13) for projecting patterned light and a sensor (12) for capturing images of the object (19). The target (17) has predetermined features visible to the sensor simultaneously with the object for enabling the processor to determine the location of the sensor with respect to the object. The generates a three-dimensional model of the object on the basis of images of the object with the patterned light projected thereon. The scanner device further comprises a light source (14) for directionally illuminating the object (19), and the sensor (12) is arranged to capture images of the illuminated object. The processor generates sets of photometric data for the object when illuminated from different directions. The processor combines the geometric data and photometric data to output a model comprising geometric information on the object together with photometric information spatially registered with the geometric information.

Abstract translation: 一种扫描仪系统和相应的方法，所述系统包括：扫描仪装置（1）; 目标17）和处理器（21）。 扫描仪装置（1）包括：用于投射图案光的发射器（13）和用于捕获物体（19）的图像的传感器（12）。 目标（17）具有与物体同时可见的预定特征，使得处理器能够确定传感器相对于物体的位置。 基于投影在其上的图案光的对象的图像，生成对象的三维模型。 扫描器装置还包括用于定向照射物体（19）的光源（14），并且传感器（12）被布置成捕获被照射物体的图像。 当从不同方向照明时，处理器生成对象的测光数据集。 处理器组合几何数据和光度数据以输出包括物体上的几何信息的模型以及与几何信息在空间上注册的光度信息。

公开(公告)号：US08401242B2

公开(公告)日：2013-03-19

申请号：US13017587

申请日：2011-01-31

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

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

IPC: G06K9/00

CPC classification number: A63F13/00 ,  A63F13/06 ,  A63F2300/1087 ,  A63F2300/69 ,  G06K9/00 ,  G06K9/00664 ,  G06T7/251 ,  G06T7/30 ,  G06T2207/10028 ,  G06T2207/10048 ,  G06T2207/30244

Abstract: Real-time camera tracking using depth maps is described. In an embodiment depth map frames are captured by a mobile depth camera at over 20 frames per second and used to dynamically update in real-time a set of registration parameters which specify how the mobile depth camera has moved. In examples the real-time camera tracking output is used for computer game applications and robotics. In an example, an iterative closest point process is used with projective data association and a point-to-plane error metric in order to compute the updated registration parameters. In an example, a graphics processing unit (GPU) implementation is used to optimize the error metric in real-time. In some embodiments, a dense 3D model of the mobile camera environment is used.

Abstract translation: 描述使用深度图的实时相机跟踪。 在一个实施例中，深度图帧由移动深度相机以每秒20帧的速度捕获，并且用于实时动态地更新一组注册参数，这些参数指定移动深度相机已经移动。 在实例中，实时相机跟踪输出用于计算机游戏应用和机器人。 在一个例子中，迭代最近点处理与投影数据关联和点到平面误差度量一起使用，以便计算更新的注册参数。 在一个例子中，使用图形处理单元（GPU）实现来实时优化误差度量。 在一些实施例中，使用移动照相机环境的密集3D模型。

公开(公告)号：US20120239174A1

公开(公告)日：2012-09-20

申请号：US13050858

申请日：2011-03-17

Applicant: Jamie Daniel Joseph Shotton ,  Pushmeet Kohli ,  Ross Brook Girshick ,  Andrew Fitzgibbon ,  Antonio Criminisi

Inventor： Jamie Daniel Joseph Shotton ,  Pushmeet Kohli ,  Ross Brook Girshick ,  Andrew Fitzgibbon ,  Antonio Criminisi

IPC: G06F19/00 ,  G06K9/62 ,  G06K9/68

CPC classification number: G06F3/017 ,  G06K9/00362 ,  G06N5/025

Abstract: 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.

Abstract translation: 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。