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    METHOD AND SYSTEM FOR RECOGNIZING AN OBJECT
    2.
    发明申请
    METHOD AND SYSTEM FOR RECOGNIZING AN OBJECT 有权
    用于识别对象的方法和系统

    公开(公告)号：US20160048727A1

    公开(公告)日：2016-02-18

    申请号：US14461057

    申请日：2014-08-15

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

    Inventor： Yongmian ZHANG ,  Hung-Shou Tai ,  Haisong Gu

    IPC: G06K9/00 ,  H04N5/33

    CPC classification number: G06K9/00389 ,  G06K9/6212 ,  H04N5/332

    Abstract: A method, a system, and a non-transitory computer readable medium for recognizing an object are disclosed, the method including: emitting an array of infrared rays from an infrared emitter towards a projection region, the projection region including a first object; generating a reference infrared image by recording an intensity of ray reflection from the projection region without the first object; generating a target infrared image by recording the intensity of ray reflection from the projection region with the first object; comparing the target infrared image to the reference infrared image to generate a predetermined intensity threshold; and extracting the first object from the target infrared image, if the intensity of ray reflection of the target infrared image of the first object exceeds the predetermined intensity threshold.

    Abstract translation: 公开了一种用于识别对象的方法，系统和非暂时计算机可读介质，所述方法包括：从红外发射器朝向投影区域发射红外线阵列，所述投影区域包括第一对象; 通过从没有第一物体的投影区域记录射线反射的强度来产生参考红外图像; 通过从第一物体记录来自投影区域的射线反射强度来产生目标红外图像; 将目标红外图像与参考红外图像进行比较以产生预定强度阈值; 并且如果第一对象的目标红外图像的射线反射强度超过预定强度阈值，则从目标红外图像提取第一对象。

    METHOD AND SYSTEM FOR 3D GESTURE BEHAVIOR RECOGNITION
    3.
    发明申请
    METHOD AND SYSTEM FOR 3D GESTURE BEHAVIOR RECOGNITION 审中-公开
    三维姿态行为识别方法与系统

    公开(公告)号：US20140145936A1

    公开(公告)日：2014-05-29

    申请号：US14090207

    申请日：2013-11-26

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

    Inventor： Haisong GU ,  Yongmian ZHANG

    IPC: G06F3/01

    CPC classification number: G06F3/017 ,  G06F3/005 ,  G06F3/011 ,  G06F3/0304 ,  G06K9/00342 ,  G06K9/6277

    Abstract: A method for 3D gesture behavior recognition is disclosed, which includes detecting a behavior change of one or more attendees at a meeting and/or conference; classifying the behavior change; and performing an action based on the behavior change of the one or more attendees. Another method, system and computer readable medium for 3D gesture behavior recognition as disclosed, includes obtaining temporal segmentation of human motion sequences for one or more attendees; determining a probability density function of the temporal segmentations of the human motion sequences using a Parzen window density estimation model; computing a bandwidth for determination of a median absolute deviation; updating the Parzen window to adapt for changes in the motion sequences for the one or more attendees; and detecting actions based.

    Abstract translation: 公开了一种用于3D手势行为识别的方法，其包括在会议和/或会议中检测一个或多个与会者的行为变化; 分类行为变化; 并且基于一个或多个参加者的行为改变来执行动作。 如所公开的用于3D手势行为识别的另一种方法，系统和计算机可读介质包括获得一个或多个与会者的人运动序列的时间分割; 使用Parzen窗口密度估计模型确定人类运动序列的时间分段的概率密度函数; 计算用于确定中值绝对偏差的带宽; 更新Parzen窗口以适应一个或多个与会者的运动顺序的变化; 并基于检测动作。

    METHOD AND SYSTEM FOR CELL IMAGE SEGMENTATION USING MULTI-STAGE CONVOLUTIONAL NEURAL NETWORKS
    4.
    发明申请
    METHOD AND SYSTEM FOR CELL IMAGE SEGMENTATION USING MULTI-STAGE CONVOLUTIONAL NEURAL NETWORKS 审中-公开

    公开(公告)号：US20190228268A1

    公开(公告)日：2019-07-25

    申请号：US16315560

    申请日：2017-08-09

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

    Inventor： Yongmian ZHANG ,  Jingwen ZHU

    IPC: G06K9/62 ,  G06N3/04 ,  G06N3/08

    Abstract: An artificial neural network system for image classification, including multiple independent individual convolutional neural networks (CNNs) connected in multiple stages, each CNN configured to process an input image to calculate a pixelwise classification. The output of an earlier stage CNN, which is a class score image having identical height and width as its input image and a depth of N representing the probabilities of each pixel of the input image belonging to each of N classes, is input into the next stage CNN as input image. When training the network system, the first stage CNN is trained using first training images and corresponding label data; then second training images are forward propagated by the trained first stage CNN to generate corresponding class score images, which are used along with label data corresponding to the second training images to train the second stage CNN.

    METHOD AND SYSTEM OF TEMPORAL SEGMENTATION FOR GESTURE ANALYSIS
    5.
    发明申请
    METHOD AND SYSTEM OF TEMPORAL SEGMENTATION FOR GESTURE ANALYSIS 有权

    公开(公告)号：US20170286760A1

    公开(公告)日：2017-10-05

    申请号：US15507735

    申请日：2015-08-27

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

    Inventor： Quentin AUGE ,  Yongmian ZHANG ,  Haisong GU

    IPC: G06K9/00

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

    Abstract: A method, system and non-transitory computer readable medium are disclosed for recognizing gestures, 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.

    METHOD AND APPARATUS FOR INTERACTIVE USER INTERFACE WITH WEARABLE DEVICE
    6.
    发明申请
    METHOD AND APPARATUS FOR INTERACTIVE USER INTERFACE WITH WEARABLE DEVICE 有权
    用于具有可接受设备的交互式用户界面的方法和装置

    公开(公告)号：US20160252976A1

    公开(公告)日：2016-09-01

    申请号：US14632661

    申请日：2015-02-26

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

    Inventor： Yongmian ZHANG ,  Jingwen ZHU ,  Toshiki OHINATA ,  Haisong GU

    IPC: G06F3/03 ,  G06K9/00 ,  H04N5/33 ,  G06F3/00 ,  G06F3/01

    CPC classification number: G06F3/0317 ,  G06F3/011 ,  G06F3/038 ,  G06K9/00375

    Abstract: A method and system are disclosed for recognizing an object, the method including emitting one or more arranged patterns of infrared rays (IR) from an infrared emitter towards a projection region, the one or more arranged patterns of infrared rays forming unique dot patterns; mapping the one or more arranged patterns of infrared rays on the operation region to generate a reference image; capturing an IR image and a RGB image of an object with a wearable device, the wearable device including an infrared (IR) camera and a RGB camera; extracting IR dots from the IR image and determining a match between the extracted IR dots and the reference image; determining a position of the RGB image on the reference image; and mapping the position of the RGB image to a coordinate on the projection region.

    Abstract translation: 公开了一种用于识别物体的方法和系统，该方法包括从红外发射器朝向投影区域发射一个或多个布置的红外线（IR）图案，所述一个或多个排列的红外线图案形成唯一的点图案; 将所述一个或多个布置的红外线图案映射在所述操作区域上以生成参考图像; 使用可穿戴装置拍摄对象的IR图像和RGB图像，所述可佩戴装置包括红外（IR）相机和RGB相机; 从IR图像提取IR点并确定提取的IR点和参考图像之间的匹配; 确定参考图像上的RGB图像的位置; 并将RGB图像的位置映射到投影区域上的坐标。

    METHOD FOR TEXT MATCHING AND CORRECTION
    7.
    发明申请
    METHOD FOR TEXT MATCHING AND CORRECTION 审中-公开

    公开(公告)号：US20200311411A1

    公开(公告)日：2020-10-01

    申请号：US16368312

    申请日：2019-03-28

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

    Inventor： Shubham AGARWAL ,  Yongmian ZHANG

    IPC: G06K9/00 ,  G06F17/27

    Abstract: A text recognition method and system involves computing a text matching score between an input text and an output candidate text. The text matching score is computed by evaluating respective N-grams of the input text and the output candidate text. The N-grams are compared in pairs for visual similarity by determining N-gram pair scores, which are used to compute the text matching score. The N-gram pair scores are determined using a set of probabilities of confusion between characters contained in the N-grams. The described approach can address inconsistent results that arise from conventional text similarity quantifiers.

    METHOD AND SYSTEM FOR EMOTION AND BEHAVIOR RECOGNITION
    8.
    发明申请
    METHOD AND SYSTEM FOR EMOTION AND BEHAVIOR RECOGNITION 有权
    用于感知和行为识别的方法和系统

    公开(公告)号：US20150186713A1

    公开(公告)日：2015-07-02

    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

    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 FOR MULTI-SCALE CELL IMAGE SEGMENTATION USING MULTIPLE PARALLEL CONVOLUTIONAL NEURAL NETWORKS
    9.
    发明申请
    METHOD AND SYSTEM FOR MULTI-SCALE CELL IMAGE SEGMENTATION USING MULTIPLE PARALLEL CONVOLUTIONAL NEURAL NETWORKS 审中-公开

    公开(公告)号：US20190236411A1

    公开(公告)日：2019-08-01

    申请号：US16315541

    申请日：2017-08-09

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

    Inventor： Jingwen ZHU ,  Yongmian ZHANG

    IPC: G06K9/62 ,  G06N3/04 ,  G06T3/40

    CPC classification number: G06K9/6256 ,  G06K9/4628 ,  G06K9/6268 ,  G06K9/6274 ,  G06K9/6292 ,  G06N3/0454 ,  G06T3/4046

    Abstract: An artificial neural network system for image classification, formed of multiple independent individual convolutional neural networks (CNNs), each CNN being configured to process an input image patch to calculate a classification for the center pixel of the patch. The multiple CNNs have different receptive field of views for processing image patches of different sizes centered at the same pixel. A final classification for the center pixel is calculated by combining the classification results from the multiple CNNs. An image patch generator is provided to generate the multiple input image patches of different sizes by cropping them from the original input image. The multiple CNNs have similar configurations, and when training the artificial neural network system, one CNN is trained first, and the learned parameters are transferred to another CNN as initial parameters and the other CNN is further trained. The classification includes three classes, namely background, foreground, and edge.

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