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公开(公告)号：US09557836B2
公开(公告)日：2017-01-31
申请号：US13286966
申请日：2011-11-01
Applicant: Toby Sharp , Jamie Daniel Joseph Shotton
Inventor： Toby Sharp , Jamie Daniel Joseph Shotton
CPC classification number: 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
Abstract: 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.
Abstract translation: 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。
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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。
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公开(公告)号：US20130106994A1
公开(公告)日：2013-05-02
申请号：US13286966
申请日：2011-11-01
Applicant: Toby Sharp , Jamie Daniel Joseph Shotton
Inventor： Toby Sharp , Jamie Daniel Joseph Shotton
IPC: H04N13/00
CPC classification number: 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
Abstract: 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.
Abstract translation: 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。
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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
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。
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公开(公告)号：US08411948B2
公开(公告)日：2013-04-02
申请号：US12718232
申请日：2010-03-05
Applicant: Carsten Curt Eckard Rother , Toby Sharp
Inventor： Carsten Curt Eckard Rother , Toby Sharp
Abstract: A method of up-sampling binary images for segmentation is described. In an embodiment, digital images are down-sampled before segmentation. The resulting initial binary segmentation, which has a lower resolution than the original image, is then up-sampled and smoothed to generate an interim non-binary solution which has a higher resolution than the initial binary segmentation. The final binary segmentation for the image is then computed from the interim non-binary solution based on a threshold. This method does not use the original image data in inferring the final binary segmentation solution from the initial binary segmentation. In an embodiment, the method may be applied to all images and in another embodiment, the method may be used for images which comprise a large number of pixels in total or in single dimension and smaller images may not be down-sampled before segmentation.
Abstract translation: 描述了用于分割的二进制图像的上采样方法。 在一个实施例中，在分割之前对数字图像进行下采样。 然后，所得到的具有比原始图像更低分辨率的初始二进制分割被上采样和平滑以产生具有比初始二进制分割更高分辨率的临时非二进制解。 然后基于阈值从临时非二进制解决方案计算图像的最终二进制分割。 该方法不使用原始图像数据从最初的二进制分割推断最终的二进制分割解决方案。 在一个实施例中，该方法可以应用于所有图像，并且在另一个实施例中，该方法可以用于总共或单维度中包含大量像素的图像，并且在分割之前可能不会对较小的图像进行下采样。
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Patent Agency Ranking
公开(公告)号：US08351654B2
公开(公告)日：2013-01-08
申请号：US12431421
申请日：2009-04-28
Applicant: Antonio Criminisi , Toby Sharp
Inventor： Antonio Criminisi , Toby Sharp
CPC classification number: G06K9/6215 , G06T11/001
Abstract: Image processing using geodesic forests is described. In an example, a geodesic forest engine determines geodesic shortest-path distances between each image element and a seed region specified in the image in order to form a geodesic forest data structure. The geodesic distances take into account gradients in the image of a given image modality such as intensity, color, or other modality. In some embodiments, a 1D processing engine carries out 1D processing along the branches of trees in the geodesic forest data structure to form a processed image. For example, effects such as ink painting, edge-aware texture flattening, contrast-aware image editing, forming animations using geodesic forests and other effects are achieved using the geodesic forest data structure. In some embodiments the geodesic forest engine uses a four-part raster scan process to achieve real-time processing speeds and parallelization is possible in many of the embodiments.
Abstract translation: 描述了使用测地森林进行图像处理。 在一个示例中，测地森林引擎确定每个图像元素与图像中指定的种子区域之间的测距最短路径距离，以形成测地森林数据结构。 测距距离考虑了给定图像形态（如强度，颜色或其他形式）图像中的渐变。 在一些实施例中，1D处理引擎沿着测地森林数据结构中的树的分支执行1D处理，以形成经处理的图像。 例如，使用测地森林数据结构实现诸如水墨绘画，边缘感知纹理平整，对比度感知图像编辑，使用测地森林形成动画等效果。 在一些实施例中，测地森林引擎使用四部分光栅扫描过程来实现实时处理速度，并且在许多实施例中并行化是可能的。
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公开(公告)号：US20120166462A1
公开(公告)日：2012-06-28
申请号：US12979362
申请日：2010-12-28
Applicant: Sayan D. Pathak , Antonio Criminisi , Steven J. White , Liqun Fu , Khan M. Siddiqui , Toby Sharp , Ender Konukoglu , Bryan Dove , Michael T. Gillam
Inventor： Sayan D. Pathak , Antonio Criminisi , Steven J. White , Liqun Fu , Khan M. Siddiqui , Toby Sharp , Ender Konukoglu , Bryan Dove , Michael T. Gillam
CPC classification number: G06F3/04845 , G06F3/04842 , G06F9/451 , G06F19/321 , G16H15/00 , G16H40/63
Abstract: The present discussion relates to automated image data processing and visualization. One example can facilitate generating a graphical user-interface (GUI) from image data that includes multiple semantically-labeled user-selectable anatomical structures. This example can receive a user selection of an individual semantically-labeled user-selectable anatomical structure. The example can locate a sub-set of the image data associated with the individual semantically-labeled user-selectable anatomical structure and can cause presentation of the sub-set of the image data on a subsequent GUI.
Abstract translation: 本发明涉及自动图像数据处理和可视化。 一个示例可以有助于从包括多个语义标记的用户可选解剖结构的图像数据生成图形用户界面（GUI）。 该示例可以接收用户选择单个语义标记的用户可选择的解剖结构。 该示例可以定位与单独的语义标记的用户可选择的解剖结构相关联的图像数据的子集，并且可以在随后的GUI上引起图像数据的子集的呈现。
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公开(公告)号：US20110141121A1
公开(公告)日：2011-06-16
申请号：US12635861
申请日：2009-12-11
Applicant: Toby Sharp , Antonio Criminisi
Inventor： Toby Sharp , Antonio Criminisi
IPC: G06F15/80
CPC classification number: G06T17/10 , A63F2300/1087 , G06F17/10 , G06T5/30 , G06T2207/20041
Abstract: Parallel processing for distance transforms is described. In an embodiment a raster scan algorithm is used to compute a distance transform such that each image element of a distance image is assigned a distance value. This distance value is a shortest distance from the image element to the seed region. In an embodiment two threads execute in parallel with a first thread carrying out a forward raster scan over the distance image and a second thread carrying out a backward raster scan over the image. In an example, a thread pauses when a cross-over condition is met until the other thread meets the condition after which both threads continue. In embodiments distances may be computed in Euclidean space or along geodesics defined on a surface. In an example, four threads execute two passes in parallel with each thread carrying out a raster scan over a different quarter of the image.
Abstract translation: 描述了距离变换的并行处理。 在一个实施例中，光栅扫描算法用于计算距离变换，使得距离图像的每个图像元素被分配距离值。 该距离值是从图像元素到种子区域的最短距离。 在一个实施例中，两个线程与第一线程并行执行，该第一线程在距离图像上执行正向光栅扫描，而第二线程在图像上执行向后光栅扫描。 在一个示例中，当满足交叉条件时，线程将暂停，直到另一个线程满足两个线程继续的条件为止。 在实施例中，距离可以在欧氏距离空间中或沿着表面上定义的测地线计算。 在一个示例中，四个线程与在每个图像的不同四分之一处执行光栅扫描的每个线程并行执行两个遍。
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公开(公告)号：US20090290795A1
公开(公告)日：2009-11-26
申请号：US12126302
申请日：2008-05-23
Applicant: Antonio Criminisi , Toby Sharp
Inventor： Antonio Criminisi , Toby Sharp
IPC: G06K9/34
CPC classification number: G06T5/002 , G06K9/342 , G06T7/11 , G06T7/155 , G06T2207/10016 , G06T2207/30212
Abstract: A method of geodesic image and video processing is proposed. In an embodiment, the method uses a geodesic distance transform to construct an image filter. The filter can be used in a variety of image editing operations such as segmentation, denoising, texture smoothing, image stitching and cartooning. In one embodiment, the method may be made efficient by utilizing parallelism of the algorithm to carry out processing steps on at least two processing cores concurrently. This efficiency may enable high-resolution images and video to be processed at ‘real time’ rates without the need for specialist hardware.
Abstract translation: 提出了一种测地图像和视频处理方法。 在一个实施例中，该方法使用测地距离变换来构造图像滤波器。 滤镜可用于各种图像编辑操作，如分割，去噪，纹理平滑，图像拼接和卡通。 在一个实施例中，可以通过利用算法的并行性来同时对至少两个处理核执行处理步骤来使该方法有效。 这种效率可以使得高分辨率图像和视频以“实时”速率被处理，而不需要专用硬件。
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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
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。
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