公开(公告)号：US20170228616A1

公开(公告)日：2017-08-10

申请号：US15017257

申请日：2016-02-05

申请人： Tolga Tasdizen ,  Mojtaba Seyedhosseini

发明人： Tolga Tasdizen ,  Mojtaba Seyedhosseini

IPC分类号： G06K9/62 ,  G06K9/00

CPC分类号： G06K9/6256 ,  G06K9/00624 ,  G06K9/00664 ,  G06K9/6227 ,  G06K9/626 ,  G06K9/6261 ,  G06K9/6268 ,  G06K9/6281 ,  G06K9/6857

摘要： An image classifier comprises a first classifier and a second classifier. The first classifier comprises L individual classifiers, which are trained at different, respective image resolutions from a first full-resolution level to a lowest-resolution level. Outputs of the first set of classifiers are used to train the second classifier at the full-resolution level. Accordingly, the second classifier exploits contextual information at multiple different image resolutions. The classifiers may be trained to optimize a joint posterior probability at multiple resolutions.

公开(公告)号：US20130094750A1

公开(公告)日：2013-04-18

申请号：US13650387

申请日：2012-10-12

申请人： Tolga Tasdizen ,  Nisha Ramesh ,  Mohamed Salama

发明人： Tolga Tasdizen ,  Nisha Ramesh ,  Mohamed Salama

IPC分类号： G06K9/34

CPC分类号： G06K9/0014

摘要： A method of identifying individual cells in an image of a cytological preparation. The method includes the steps of obtaining an image of a cytological preparation including a plurality of cells; identifying a first region of the image, the first region having a region boundary encompassing at least one lobe, wherein the first region includes at least one cell; detecting at least one circle within the first region, where the at least one circle substantially covers the at least one lobe of the first region; and if the first region has more than one circle, splitting the region into at least two subregions.

摘要翻译： 鉴定细胞学准备图像中的各个细胞的方法。 该方法包括获得包括多个细胞的细胞学制剂的图像的步骤; 识别图像的第一区域，所述第一区域具有包围至少一个凸角的区域边界，其中所述第一区域包括至少一个单元; 检测所述第一区域内的至少一个圆，其中所述至少一个圆基本上覆盖所述第一区域的所述至少一个凸角; 并且如果第一区域具有多于一个圆，则将区域分成至少两个子区域。

公开(公告)号：US07542604B2

公开(公告)日：2009-06-02

申请号：US11205729

申请日：2005-08-17

申请人： Leo Grady ,  Tolga Tasdizen ,  Ross Whitaker

发明人： Leo Grady ,  Tolga Tasdizen ,  Ross Whitaker

IPC分类号： G06K9/34

CPC分类号： G06T7/143 ,  G06T7/12 ,  G06T2207/20016 ,  G06T2207/20101 ,  G06T2207/30004

摘要： A method of segmenting a digitized image includes marking a subset of pixels in an image, defining edge conductances between each pair of adjacent pixels in the image based on the intensity difference of each said pixel pair, associating a probability potential with each unmarked pixel, and using a multigrid method to solve for the probability potentials for each unmarked pixel, wherein a restriction operator from the image grid to a coarse grid is calculated from a conductance-weighted average of the conductances on the image grid, the coarse grid conductances are calculated from the image grid conductances using a Δ-Y conversion, and the multigrid prolongation operator is calculated using a conductance-weighted interpolation of the coarse grid conductances.

摘要翻译： 分割数字化图像的方法包括标记图像中的像素子集，基于每个所述像素对的强度差定义图像中每对相邻像素之间的边缘电导，将概率电位与每个未标记像素相关联，以及 使用多网格方法来求解每个未标记像素的概率电位，其中根据图像网格上的电导的电导加权平均来计算从图像网格到粗网格的限制算子，粗网格电导从 使用Delta-Y转换的图像格栅电导，并且使用粗网格电导的电导加权插值来计算多网格延长算子。

公开(公告)号：US20060050959A1

公开(公告)日：2006-03-09

申请号：US11205729

申请日：2005-08-17

申请人： Leo Grady ,  Tolga Tasdizen ,  Ross Whitaker

发明人： Leo Grady ,  Tolga Tasdizen ,  Ross Whitaker

IPC分类号： G06K9/34

CPC分类号： G06T7/143 ,  G06T7/12 ,  G06T2207/20016 ,  G06T2207/20101 ,  G06T2207/30004

摘要： A method of segmenting a digitized image includes marking a subset of pixels in an image, defining edge conductances between each pair of adjacent pixels in the image based on the intensity difference of each said pixel pair, associating a probability potential with each unmarked pixel, and using a multigrid method to solve for the probability potentials for each unmarked pixel, wherein a restriction operator from the image grid to a coarse grid is calculated from a conductance-weighted average of the conductances on the image grid, the coarse grid conductances are calculated from the image grid conductances using a Δ-Y conversion, and the multigrid prolongation operator is calculated using a conductance-weighted interpolation of the coarse grid conductances.

公开(公告)号：US08412651B2

公开(公告)日：2013-04-02

申请号：US12875330

申请日：2010-09-03

申请人： Antonio Rafael Paiva ,  Tolga Tasdizen

发明人： Antonio Rafael Paiva ,  Tolga Tasdizen

IPC分类号： G06N5/00

CPC分类号： G06N99/005 ,  G06K9/6248

摘要： A method, a system, and a computer-readable medium are provided for characterizing a dataset. A representative dataset is defined from a dataset by a computing device. The representative dataset includes a first plurality of data points and the dataset includes a second plurality of data points. The number of the first plurality of data points is less than the number of the second plurality of data points. The data point is added to the representative dataset if a minimum distance between the data point and each data point of the representative dataset is greater than a sampling parameter. The data point is added to a refinement dataset if the minimum distance between the data point and each data point of the representative dataset is less than the sampling parameter and greater than half the sampling parameter. A weighting matrix is defined by the computing device that includes a weight value calculated for each of the first plurality of data points based on a determined number of the second plurality of data points associated with a respective data point of the first plurality of data points. The weight value for a closest data point of the representative dataset is updated if the minimum distance between the data point and each data point of the representative dataset is less than half the sampling parameter. A machine learning algorithm is executed by the computing device using the defined representative dataset and the defined weighting matrix applied in an approximation for a computation of a full kernel matrix of the dataset to generate a parameter characterizing the dataset.

摘要翻译： 提供了一种用于表征数据集的方法，系统和计算机可读介质。 代表数据集由计算设备从数据集定义。 代表数据集包括第一多个数据点，并且数据集包括第二多个数据点。 第一多个数据点的数量小于第二多个数据点的数量。 如果代表数据集的数据点和每个数据点之间的最小距离大于采样参数，则将数据点添加到代表数据集。 如果数据点与代表数据集的每个数据点之间的最小距离小于采样参数并且大于采样参数的一半，则将数据点添加到细化数据集。 基于与第一多个数据点的相应数据点相关联的第二多个数据点的确定数量，计算设备定义加权矩阵，该加权矩阵包括针对第一多个数据点中的每一个计算的加权值。 如果代表数据集的数据点和每个数据点之间的最小距离小于采样参数的一半，则更新代表数据集最接近的数据点的权重值。 机器学习算法由计算设备使用定义的代表数据集执行，并且定义的加权矩阵在近似中应用于计算数据集的完整内核矩阵，以生成表征数据集的参数。

公开(公告)号：US20120059777A1

公开(公告)日：2012-03-08

申请号：US12875330

申请日：2010-09-03

申请人： Antonio Rafael da Costa Paiva ,  Tolga Tasdizen

发明人： Antonio Rafael da Costa Paiva ,  Tolga Tasdizen

IPC分类号： G06F15/18

CPC分类号： G06N99/005 ,  G06K9/6248

摘要： A method, a system, and a computer-readable medium are provided for characterizing a dataset. A representative dataset is defined from a dataset by a computing device. The representative dataset includes a first plurality of data points and the dataset includes a second plurality of data points. The number of the first plurality of data points is less than the number of the second plurality of data points. The data point is added to the representative dataset if a minimum distance between the data point and each data point of the representative dataset is greater than a sampling parameter. The data point is added to a refinement dataset if the minimum distance between the data point and each data point of the representative dataset is less than the sampling parameter and greater than half the sampling parameter. A weighting matrix is defined by the computing device that includes a weight value calculated for each of the first plurality of data points based on a determined number of the second plurality of data points associated with a respective data point of the first plurality of data points. The weight value for a closest data point of the representative dataset is updated if the minimum distance between the data point and each data point of the representative dataset is less than half the sampling parameter. A machine learning algorithm is executed by the computing device using the defined representative dataset and the defined weighting matrix applied in an approximation for a computation of a full kernel matrix of the dataset to generate a parameter characterizing the dataset.

摘要翻译： 提供了一种用于表征数据集的方法，系统和计算机可读介质。 代表数据集由计算设备从数据集定义。 代表数据集包括第一多个数据点，并且数据集包括第二多个数据点。 第一多个数据点的数量小于第二多个数据点的数量。 如果代表数据集的数据点和每个数据点之间的最小距离大于采样参数，则将数据点添加到代表数据集。 如果数据点与代表数据集的每个数据点之间的最小距离小于采样参数并且大于采样参数的一半，则将数据点添加到细化数据集。 基于与第一多个数据点的相应数据点相关联的第二多个数据点的确定数量，计算设备定义加权矩阵，该加权矩阵包括针对第一多个数据点中的每一个计算的加权值。 如果代表数据集的数据点和每个数据点之间的最小距离小于采样参数的一半，则更新代表数据集最接近的数据点的权重值。 机器学习算法由计算设备使用定义的代表数据集执行，并且定义的加权矩阵在近似中应用于计算数据集的完整内核矩阵，以生成表征数据集的参数。

公开(公告)号：US20110274356A1

公开(公告)日：2011-11-10

申请号：US13129110

申请日：2009-11-12

申请人： Tolga Tasdizen ,  Antonio R. C. Paiva

发明人： Tolga Tasdizen ,  Antonio R. C. Paiva

IPC分类号： G06K9/46

CPC分类号： G06K9/4647 ,  G06K9/00067

摘要： Image pattern recognition is described. In accordance with one embodiment, a method for image recognition includes dividing an image into blocks in preparation for separating a region of interest of the image from the remainder of the image. The blocks can be analyzed to determine whether a two dimensional projection of data from one or more blocks has a circular shape. The region of interest can be identified by identifying the blocks with circular shaped projections.

摘要翻译： 描述图像模式识别。 根据一个实施例，一种用于图像识别的方法包括将图像划分成块，以准备将图像的感兴趣区域与图像的其余部分分离。 可以分析块以确定来自一个或多个块的数据的二维投影是否具有圆形。 可以通过识别具有圆形突起的块来识别感兴趣的区域。