公开(公告)号：US20100226547A1

公开(公告)日：2010-09-09

申请号：US12396590

申请日：2009-03-03

Applicant: Antonio Criminisi ,  Evgeny Salnikov ,  Toby Sharp

Inventor： Antonio Criminisi ,  Evgeny Salnikov ,  Toby Sharp

IPC: G06K9/00

CPC classification number: G06K9/38 ,  G06T5/009 ,  G06T5/40 ,  G06T2207/10032 ,  G06T2207/10081 ,  G06T2207/20208 ,  G06T2207/30004 ,  G06T2207/30181

Abstract: A system for multi-modal mapping of images is described. Embodiments are described where the image mapping system is used for visualizing high dynamic range images such as medical images, satellite images, high dynamic range photographs and the like and also for compressing such images. In examples, high bit-depth images are tone-mapped for display on equipment of lower bit-depth without loss of detail. In embodiments, the image mapping system computes statistics describing an input image and fits a multi-modal model to those statistics efficiently. In embodiments, the multi-modal model is a Gaussian mixture model and a plurality of sigmoid functions corresponding to the multi-modal model are obtained. In an embodiment the sigmoid functions are added to form a tone-mapping function which is used to transform a high bit-depth image such as 16 or 12 bits per pixel to a low bit-depth image such as 8 bits per pixel.

Abstract translation: 描述了用于图像的多模态映射的系统。 描述实施例，其中图像映射系统用于可视化诸如医学图像，卫星图像，高动态范围照片等的高动态范围图像，并且还用于压缩这样的图像。 在示例中，高位深图像被色调映射以便在较低位深度的设备上显示而不损失细节。 在实施例中，图像映射系统计算描述输入图像的统计量，并将多模态模型有效地适应于这些统计。 在实施例中，多模态模型是高斯混合模型，并且获得对应于多模态模型的多个S形函数。 在一个实施例中，添加S形功能以形成色调映射功能，其用于将诸如每像素16或12位的高位深度图像变换为诸如每像素8位的低位深度图像。

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

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

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：US08655069B2

公开(公告)日：2014-02-18

申请号：US12718343

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: G06K9/34

CPC classification number: G06K9/34

Abstract: Methods of updating image segmentation following user input are described. In an embodiment, the properties used in computing the different portions of the image are updated as a result of one or more user inputs. Image elements which have been identified by a user input are given more weight when updating the properties than other image elements which have already been assigned to a particular portion of the image. In another embodiment, an updated segmentation is post-processed such that only regions which are connected to an appropriate user input are updated.

Abstract translation: 描述用户输入后更新图像分割的方法。 在一个实施例中，用于计算图像的不同部分的属性被更新为一个或多个用户输入的结果。 当已经被分配给图像的特定部分的其他图像元素更新时，由用户输入识别的图像元素被赋予更多的权重。 在另一个实施例中，更新的分段被后处理，使得仅更新连接到适当的用户输入的区域。

公开(公告)号：US20100272367A1

公开(公告)日：2010-10-28

申请号：US12431421

申请日：2009-04-28

Applicant: Antonio Criminisi ,  Toby Sharp

Inventor： Antonio Criminisi ,  Toby Sharp

IPC: G06K9/46

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。

公开(公告)号：US20100094800A1

公开(公告)日：2010-04-15

申请号：US12248536

申请日：2008-10-09

Applicant: Toby Sharp

Inventor： Toby Sharp

IPC: G06F17/00

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：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。