公开(公告)号：US09177416B2

公开(公告)日：2015-11-03

申请号：US12728921

申请日：2010-03-22

Applicant: Toby Sharp

Inventor： Toby Sharp

IPC: G06T15/08 ,  G06T15/00 ,  G06T15/06 ,  H04N13/02

CPC classification number: G06T15/06 ,  G06T15/08 ,  H04N13/275

Abstract: Space skipping for multi-dimensional image rendering is described. In an embodiment a ray-casting engine is used to form a two dimensional image from an at least three dimensional image volume by computing rays extending from a camera location, through the two dimensional image and into the volume. For example, a space skipping logic is used to clip the rays such that computationally expensive aspects of ray-casting only need to be performed along the clipped rays. For example a volume pyramid is formed by repeatedly reducing the resolution of the volume data. In an example, each ray is intersected with the lowest resolution volume of the pyramid and clipped using data from that volume. In examples, the clipping is then repeated at higher resolutions in order to clip the ray closely to non-transparent voxels in the volume and optimize the task of rendering the image.

Abstract translation: 描述了用于多维图像渲染的空间跳过。 在一个实施例中，射线铸造引擎用于通过计算从相机位置延伸通过二维图像并进入体积的从至少三维图像体积形成二维图像。 例如，空间跳过逻辑用于剪切光线，使得射线投射的计算上昂贵的方面仅需要沿着剪切的射线执行。 例如，通过重复地降低体数据的分辨率来形成体积金字塔。 在一个示例中，每个射线与金字塔的最低分辨率体积相交，并使用来自该卷的数据进行剪切。 在示例中，然后以更高分辨率重复剪辑，以将光线紧密地剪切到体积中的不透明体素，并优化渲染图像的任务。

公开(公告)号：US08422769B2

公开(公告)日：2013-04-16

申请号：US12718321

申请日：2010-03-05

Applicant: Carsten Curt Eckard Rother ,  Toby Sharp ,  Andrew Blake ,  Vladimir Kolmogorov

Inventor： Carsten Curt Eckard Rother ,  Toby Sharp ,  Andrew Blake ,  Vladimir Kolmogorov

IPC: G06K9/62

CPC classification number: G06K9/34 ,  G06K9/62

Abstract: Methods of image segmentation using reduced foreground training data are described. In an embodiment, the foreground and background training data for use in segmentation of an image is determined by optimization of a modified energy function. The modified energy function is the energy function used in image segmentation with an additional term comprising a scalar value. The optimization is performed for different values of the scalar to produce multiple initial segmentations and one of these segmentations is selected based on pre-defined criteria. The training data is then used in segmenting the image. In other embodiments further methods are described: one places an ellipse inside the user-defined bounding box to define the background training data and another uses a comparison of properties of neighboring image elements, where one is outside the user-defined bounding box, to reduce the foreground training data.

Abstract translation: 描述使用减少的前景训练数据的图像分割方法。 在一个实施例中，用于图像分割的前景和背景训练数据通过改进的能量函数的优化来确定。 修改的能量函数是在图像分割中使用的能量函数，附加项包括标量值。 对标量的不同值执行优化以产生多个初始分段，并且基于预定义的标准来选择这些分段之一。 然后训练数据用于分割图像。 在其他实施例中，描述了进一步的方法：一个将椭圆放置在用户定义的边界框内以定义背景训练数据，另一个使用相邻图像元素的属性的比较，其中一个在用户定义的界限框之外，以减少 前台训练数据。

公开(公告)号：US20110216965A1

公开(公告)日：2011-09-08

申请号：US12718321

申请日：2010-03-05

Applicant: Carsten Curt Eckard Rother ,  Toby Sharp ,  Andrew Blake ,  Vladimir Kolmogorov

Inventor： Carsten Curt Eckard Rother ,  Toby Sharp ,  Andrew Blake ,  Vladimir Kolmogorov

IPC: G06K9/62

CPC classification number: G06K9/34 ,  G06K9/62

Abstract: Methods of image segmentation using reduced foreground training data are described. In an embodiment, the foreground and background training data for use in segmentation of an image is determined by optimization of a modified energy function. The modified energy function is the energy function used in image segmentation with an additional term comprising a scalar value. The optimization is performed for different values of the scalar to produce multiple initial segmentations and one of these segmentations is selected based on pre-defined criteria. The training data is then used in segmenting the image. In other embodiments further methods are described: one places an ellipse inside the user-defined bounding box to define the background training data and another uses a comparison of properties of neighboring image elements, where one is outside the user-defined bounding box, to reduce the foreground training data.

Abstract translation: 描述使用减少的前景训练数据的图像分割方法。 在一个实施例中，用于图像分割的前景和背景训练数据通过改进的能量函数的优化来确定。 修改的能量函数是在图像分割中使用的能量函数，附加项包括标量值。 对标量的不同值执行优化以产生多个初始分段，并且基于预定义的标准来选择这些分段之一。 然后训练数据用于分割图像。 在其他实施例中，描述了进一步的方法：一个将椭圆放置在用户定义的边界框内以定义背景训练数据，另一个使用相邻图像元素的属性的比较，其中一个在用户定义的界限框之外，以减少 前台训练数据。

公开(公告)号：US09256982B2

公开(公告)日：2016-02-09

申请号：US12725811

申请日：2010-03-17

Applicant: Toby Sharp ,  Antonio Criminisi ,  Khan Mohammad Siddiqui

Inventor： Toby Sharp ,  Antonio Criminisi ,  Khan Mohammad Siddiqui

IPC: G06T15/30 ,  G06T15/00 ,  G06T19/00 ,  G06K9/00

CPC classification number: G06T19/00 ,  G06T15/30 ,  G06T2200/24 ,  G06T2207/10081 ,  G06T2210/12 ,  G06T2219/004 ,  G06T2219/2012

Abstract: Medical image rendering is described. In an embodiment a medical image visualization engine receives results from an organ recognition system which provide estimated organ centers, bounding boxes and organ classification labels for a given medical image. In examples the visualization engine uses the organ recognition system results to select appropriate transfer functions, bounding regions, clipping planes and camera locations in order to optimally view an organ. For example, a rendering engine uses the selections to render a two-dimensional image of medical diagnostic quality with minimal user input. In an embodiment a graphical user interface populates a list of organs detected in a medical image and a clinician is able to select one organ and immediately be presented with the optimal view of that organ. In an example opacity of background regions of the medical image may be adjusted to provide context for organs presented in a foreground region.

Abstract translation: 描述医学图像呈现。 在一个实施例中，医学图像可视化引擎从提供给定医学图像的估计的器官中心，边界框和器官分类标签的器官识别系统接收结果。 在示例中，可视化引擎使用器官识别系统结果来选择适当的传递函数，边界区域，剪切平面和相机位置，以便最佳地观察器官。 例如，渲染引擎使用选择来以最小的用户输入呈现医学诊断质量的二维图像。 在一个实施例中，图形用户界面填充在医学图像中检测到的器官的列表，并且临床医生能够选择一个器官并且立即呈现该器官的最佳视图。 在示例性医学图像的背景区域的不透明度可以被调整以提供前景区域中呈现的器官的上下文。

公开(公告)号：US08786616B2

公开(公告)日：2014-07-22

申请号：US12635861

申请日：2009-12-11

Applicant: Toby Sharp ,  Antonio Criminisi

Inventor： Toby Sharp ,  Antonio Criminisi

IPC: G06F15/80 ,  G06K9/34 ,  G06K9/46

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。

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：US08437570B2

公开(公告)日：2013-05-07

申请号：US12126302

申请日：2008-05-23

Applicant: Antonio Criminisi ,  Toby Sharp

Inventor： Antonio Criminisi ,  Toby Sharp

IPC: G06K9/42 ,  G06K9/44 ,  G06K9/40

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。

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。

公开(公告)号：US08290882B2

公开(公告)日：2012-10-16

申请号：US12248536

申请日：2008-10-09

Applicant: Toby Sharp

Inventor： Toby Sharp

IPC: G06E1/00

CPC classification number: G06N99/005

Abstract: Methods and apparatus for evaluating decision trees on a GPU are described. In an embodiment, the structure of a decision tree is converted into a 2D “tree” array with each row representing a node in the tree. Each row comprises details of any child nodes and the parameters which are required to perform the binary test at the node. A pixel shader can then be used to evaluate the decision tree in parallel for each input data point in an input array by navigating through rows in the 2D tree array. For each row, data is read from the input array dependent upon the parameters in the row and the shader moves to another row dependent upon the result of the binary test. On reaching a row which represents a leaf node, the pixel shader outputs evaluation results, such as a leaf node index or a probability distribution over classes.

Abstract translation: 描述了用于评估GPU上的决策树的方法和装置。 在一个实施例中，将决策树的结构转换为2D树数组，每行表示树中的节点。 每行包括任何子节点的细节和在节点执行二进制测试所需的参数。 然后可以使用像素着色器通过在2D树数组中的行进行导航来并行计算输入数组中每个输入数据点的决策树。 对于每一行，取决于行中的参数，从输入数组读取数据，并且着色器根据二进制测试的结果移动到另一行。 在到达表示叶节点的行时，像素着色器输出评估结果，例如叶节点索引或类上的概率分布。

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。