中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

22 records (took 0.018 seconds)

    METHOD AND SYSTEM FOR DETECTION AND CLASSIFICATION OF CELLS USING CONVOLUTIONAL NEURAL NETWORKS
    2.
    发明申请
    METHOD AND SYSTEM FOR DETECTION AND CLASSIFICATION OF CELLS USING CONVOLUTIONAL NEURAL NETWORKS 审中-公开

    公开(公告)号：US20190065817A1

    公开(公告)日：2019-02-28

    申请号：US15690037

    申请日：2017-08-29

    Applicant: KONICA MINOLTA LABORATORY U.S.A., INC.

    Inventor： Maral Mesmakhosroshahi ,  Shubham Agarwal ,  Yongmian Zhang

    IPC: G06K9/00 ,  G06K9/62

    Abstract: An artificial neural network system implemented on a computer for cell segmentation and classification of biological images. It includes a deep convolutional neural network as a feature extraction network, a first branch network connected to the feature extraction network to perform cell segmentation, and a second branch network connected to the feature extraction network to perform cell classification using the cell segmentation map generated by the first branch network. The feature extraction network is a modified VGG network where each convolutional layer uses multiple kernels of different sizes. The second branch network takes feature maps from two levels of the feature extraction network, and has multiple fully connected layers to independently process multiple cropped patches of the feature maps, the cropped patches being located at a centered and multiple shifted positions relative to the cell being classified; a voting method is used to determine the final cell classification.

    Method and system for recognition of abnormal behavior
    5.
    发明授权
    Method and system for recognition of abnormal behavior 有权
    识别异常行为的方法和系统

    公开(公告)号：US09355306B2

    公开(公告)日：2016-05-31

    申请号：US14039437

    申请日：2013-09-27

    Applicant: KONICA MINOLTA LABORATORY U.S.A., INC.

    Inventor： Dongdong Wu ,  Yongmian Zhang ,  Haisong Gu

    IPC: G06K9/42 ,  G06K9/00

    CPC classification number: G06K9/00342 ,  G06K2009/00738

    Abstract: A method for recognizing abnormal behavior is disclosed, the method includes: capturing at least one video stream of data on one or more subjects; extracting body skeleton data from the at least one video stream of data; classifying the extracted body skeleton data as normal behavior or abnormal behavior; and generating an alert, if the extracted skeleton data is classified as abnormal behavior.

    Abstract translation: 公开了一种用于识别异常行为的方法，所述方法包括：在一个或多个主体上捕获至少一个视频数据流; 从所述至少一个视频数据流中提取身体骨骼数据; 将提取的身体骨骼数据分类为正常行为或异常行为; 并且如果所提取的骨架数据被分类为异常行为，则生成警报。

    TEXT LINE SEGMENTATION METHOD
    6.
    发明申请
    TEXT LINE SEGMENTATION METHOD 审中-公开

    公开(公告)号：US20190163971A1

    公开(公告)日：2019-05-30

    申请号：US15828110

    申请日：2017-11-30

    Applicant: KONICA MINOLTA LABORATORY U.S.A., INC.

    Inventor： Shubham Agarwal ,  Yongmian Zhang

    IPC: G06K9/00 ,  G06K9/62 ,  G06K9/46 ,  G06T11/20

    Abstract: In a text line segmentation process, connected components (CCs) in document image are categorized into three subsets (normal, large, small) based on their sizes. The centroids of the normal size CCs are used to perform line detection using Hough transform. Among the detected candidate lines, those with line bounding box heights greater than a certain height are removed. For each normal size CC, if its bounding box does not overlap the bounting box of any line with an overlap area greater than a predefined fraction of the CC bounding box, a new line is added for this CC, which passes through the centroid of the CC and has an average slant angle. Each large size CCs are broken into two or more CCs. All CCs are then assigned to the nearest lines. A refinement method is also described, which can take any text line segmentation result and refine it.

    Method and system for detection and classification of cells using convolutional neural networks
    7.
    发明授权
    Method and system for detection and classification of cells using convolutional neural networks 有权

    公开(公告)号：US10282589B2

    公开(公告)日：2019-05-07

    申请号：US15690037

    申请日：2017-08-29

    Applicant: KONICA MINOLTA LABORATORY U.S.A., INC.

    Inventor： Maral Mesmakhosroshahi ,  Shubham Agarwal ,  Yongmian Zhang

    IPC: G06K9/00 ,  G06K9/62

    Abstract: An artificial neural network system implemented on a computer for cell segmentation and classification of biological images. It includes a deep convolutional neural network as a feature extraction network, a first branch network connected to the feature extraction network to perform cell segmentation, and a second branch network connected to the feature extraction network to perform cell classification using the cell segmentation map generated by the first branch network. The feature extraction network is a modified VGG network where each convolutional layer uses multiple kernels of different sizes. The second branch network takes feature maps from two levels of the feature extraction network, and has multiple fully connected layers to independently process multiple cropped patches of the feature maps, the cropped patches being located at a centered and multiple shifted positions relative to the cell being classified; a voting method is used to determine the final cell classification.

    Method and system for emotion and behavior recognition
    9.
    发明授权
    Method and system for emotion and behavior recognition 有权
    情感和行为识别的方法和系统

    公开(公告)号：US09489570B2

    公开(公告)日：2016-11-08

    申请号：US14145132

    申请日：2013-12-31

    Applicant: KONICA MINOLTA LABORATORY U.S.A., INC.

    Inventor： Chen Cao ,  Yongmian Zhang ,  Haisong Gu

    IPC: G06K9/00 ,  G06K9/62 ,  G06T7/00 ,  G06K9/46 ,  G06T7/20 ,  G06K9/32

    CPC classification number: G06K9/00342 ,  G06K9/00369 ,  G06K9/00718 ,  G06K9/00771 ,  G06K9/3241 ,  G06K9/4642 ,  G06K9/469 ,  G06K9/6212 ,  G06T7/20 ,  G06T2207/10016 ,  G06T2207/20076 ,  G06T2207/30196

    Abstract: A method and system for recognizing behavior is disclosed, the method includes: capturing at least one video stream of data on one or more subjects; extracting body skeleton data from the at least one video stream of data; computing feature extractions on the extracted body skeleton data to generate a plurality of 3 dimensional delta units for each frame of the extracted body skeleton data; generating a plurality of histogram sequences for each frame by projecting the plurality of 3 dimensional delta units for each frame to a spherical coordinate system having a plurality of spherical bins; generating an energy map for each of the plurality of histogram sequences by mapping the plurality of spherical bins versus time; applying a Histogram of Oriented Gradients (HOG) algorithm on the plurality of energy maps to generate a single column vector; and classifying the single column vector as a behavior and/or emotion.

    Abstract translation: 公开了一种用于识别行为的方法和系统，所述方法包括：在一个或多个主题上捕获至少一个视频数据流; 从所述至少一个视频数据流中提取身体骨骼数据; 对所提取的身体骨骼数据进行计算特征提取，以生成所提取的身体骨骼数据的每个帧的多个3维增量单位; 通过将每个帧的多个三维三角洲单元投影到具有多个球形仓的球面坐标系统来为每个帧生成多个直方图序列; 通过将所述多个球形仓相对于时间映射来生成所述多个直方图序列中的每一个的能量图; 在多个能量图上应用定向梯度（HOG）直方图以产生单列向量; 并将单列向量分类为行为和/或情感。

    METHOD AND SYSTEM OF TEMPORAL SEGMENTATION FOR GESTURE ANALYSIS
    10.
    发明申请
    METHOD AND SYSTEM OF TEMPORAL SEGMENTATION FOR GESTURE ANALYSIS 审中-公开
    方法分析的时间分割方法与系统

    公开(公告)号：US20160078287A1

    公开(公告)日：2016-03-17

    申请号：US14473679

    申请日：2014-08-29

    Applicant: Konica Minolta Laboratory U.S.A., Inc.

    Inventor： Quentin AUGE ,  Yongmian Zhang ,  Haisong Gu

    IPC: G06K9/00 ,  G06K9/46 ,  G06K9/62

    CPC classification number: G06K9/00342 ,  G06K9/00201 ,  G06K9/00765

    Abstract: A method, system and non-transitory computer readable medium for recognizing gestures are disclosed, the method includes capturing at least one three-dimensional (3D) video stream of data on a subject; extracting a time-series of skeletal data from the at least one 3D video stream of data; isolating a plurality of points of abrupt content change called temporal cuts, the plurality of temporal cuts defining a set of non-overlapping adjacent segments partitioning the time-series of skeletal data; identifying among the plurality of temporal cuts, temporal cuts of the time-series of skeletal data having a positive acceleration; and classifying each of the one or more pair of consecutive cuts with the positive acceleration as a gesture boundary.

    Abstract translation: 公开了一种用于识别手势的方法，系统和非暂时性计算机可读介质，所述方法包括：在对象上捕获数据的至少一个三维（3D）视频流; 从所述至少一个3D视频数据流中提取骨架数据的时间序列; 分离称为时间切割的多个突变内容变化点，所述多个时间切割定义了划分所述时间序列骨骼数据的一组不重叠的相邻分段; 在多个时间切割之间识别具有正加速度的骨架数据的时间序列的时间切割; 并且将正加速度的一个或多个连续切片中的每一个分类为手势边界。

Patent Agency Ranking