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    • 4. 发明授权
    • Operational control method, program, and recording media for robot device, and robot device
    • 机器人装置的操作控制方法,程序和记录介质,以及机器人装置
    • US06697711B2
    • 2004-02-24
    • US10258110
    • 2002-10-18
    • Jun YokonoKohtaro SabeGabriel CostaTakeshi Ohashi
    • Jun YokonoKohtaro SabeGabriel CostaTakeshi Ohashi
    • G06F1900
    • G06N3/008
    • A robot apparatus (1) includes leg blocks (3A to 3D), head block (4), etc. as a moving part (16), a motion controller (102), learning unit (103), prediction unit (104) and a drive unit (105). When the moving part (106), any of the blocks, is operated from outside, the learning unit (103) learns a time-series signal generated due to the external operation. The motion controller (102) and drive unit (105) control together the moving part (106) based on a signal generated at the moving part (106) due to an external force applied to the robot apparatus (1) and a signal having already been learned by the learning unit (103) to make an action taught by the user. The prediction unit (105) predicts whether the moving part (106) makes the taught action according to the initial signal generated at the moving part (106) due to the applied external force. Thus, the robot apparatus (1) can learn an action taught by the user and determine an external force-caused signal to make the taught action.
    • 机器人装置(1)包括作为移动部件(16)的腿部块(3A至3D),头部块(4)等,运动控制器(102),学习单元(103),预测单元(104)和 驱动单元(105)。 当移动部分(106),任何块,从外部操作时,学习单元(103)学习由于外部操作而产生的时间序列信号。 运动控制器(102)和驱动单元(105)基于由于施加到机器人装置(1)的外力而在运动部件(106)处产生的信号,以及已经具有的信号,一起控制运动部件(106) 被学习单元(103)学习以进行用户教导的动作。 预测单元(105)根据施加的外力来预测移动部件(106)是否根据在移动部件(106)产生的初始信号进行教导动作。 因此,机器人装置(1)可以学习用户教导的动作,并确定外力产生的信号以进行教导动作。
    • 6. 发明授权
    • Robot device and method of controlling robot device operation
    • 机器人装置及控制机器人装置操作的方法
    • US06865446B2
    • 2005-03-08
    • US10258152
    • 2002-02-21
    • Jun YokonoMasahiro FujitaVincent Hugel
    • Jun YokonoMasahiro FujitaVincent Hugel
    • A63H11/00B25J5/00B25J9/16B25J13/00B25J13/08G01L5/00G05B19/00
    • B25J9/1633
    • A robot apparatus is provided which includes body portions such as a head block (4), leg blocks (3A to 3D), an actuator (25) to actuate the body portions and a CPU (10) to supply a control signal to the actuator (25). In this apparatus, information about an external force applied to the apparatus, such as the position, magnitude, direction, etc. of the external force, is computed on the basis of changes of the control signal supplied from the CPU (10) to drive the actuator (25) and a signal supplied as a response to the CPU (10) when the actuator (25) is driven. The external force information is supplied to the CPU (10) and used as information for selection of a behavior and emotion of the robot apparatus and the next behavior of the apparatus. Thus, in the robot apparatus, since the external force information is computed on the basis of an output from the CPU (10) and outputs from the actuator (25) and a potentiometer (26), so an external force can be detected by the use of existing devices and no dedicated sensors have to be additionally provided.
    • 提供了一种机器人装置,其包括主体部分,例如头部块(4),腿部块(3A至3D),用于致动主体部分的致动器(25)和用于向致动器提供控制信号的CPU(10) (25)。 在该装置中,基于从CPU(10)提供到驱动器的控制信号的变化来计算关于施加到装置的外力(例如外力的位置,大小,方向等)的信息 致动器(25)和当致动器(25)被驱动时作为对CPU(10)的响应而提供的信号。 外部力信息被提供给CPU(10)并且用作用于选择机器人装置的行为和情感的信息以及装置的下一个行为。 因此,在机器人装置中,由于基于来自CPU(10)的输出并且从致动器(25)和电位计(26)输出的外力信息被计算,所以可以通过 必须另外提供现有设备的使用和专用的传感器。
    • 8. 发明授权
    • Systems and methods for segmenting digital images
    • 用于分割数字图像的系统和方法
    • US08345976B2
    • 2013-01-01
    • US12852096
    • 2010-08-06
    • Su WangShengyang DaiAkira NakamuraTakeshi OhashiJun Yokono
    • Su WangShengyang DaiAkira NakamuraTakeshi OhashiJun Yokono
    • G06K9/34
    • G06K9/6256G06K9/0014G06T7/0012G06T7/11G06T7/136G06T7/187G06T2207/30024
    • Methods and systems disclosed herein provide the capability to automatically process digital pathology images quickly and accurately. According to one embodiment, an digital pathology image segmentation task may be divided into at least two parts. An image segmentation task may be carried out utilizing both bottom-up analysis to capture local definition of features and top-down analysis to use global information to eliminate false positives. In some embodiments, an image segmentation task is carried out using a “pseudo-bootstrapping” iterative technique to produce superior segmentation results. In some embodiments, the superior segmentation results produced by the pseudo-bootstrapping method are used as input in a second segmentation task that uses a combination of bottom-up and top-down analysis.
    • 本文公开的方法和系统提供了快速且准确地自动处理数字病理图像的能力。 根据一个实施例,数字病理图像分割任务可以被划分为至少两部分。 图像分割任务可以利用自下而上的分析来捕获特征的局部定义和自上而下的分析,以使用全局信息来消除假阳性。 在一些实施例中,使用伪自举迭代技术来执行图像分割任务以产生优异的分割结果。 在一些实施例中,通过伪自举方法产生的优越分割结果被用作使用自下而上和自顶向下分析的组合的第二分段任务中的输入。
    • 10. 发明申请
    • FACE IMAGE PROCESSING APPARATUS, FACE IMAGE PROCESSING METHOD, AND COMPUTER PROGRAM
    • 面部图像处理设备,脸部图像处理方法和计算机程序
    • US20090060290A1
    • 2009-03-05
    • US12191610
    • 2008-08-14
    • Kohtaro SABEAtsushi OkuboJun Yokono
    • Kohtaro SABEAtsushi OkuboJun Yokono
    • G06K9/00
    • G06K9/00248
    • A face image processing apparatus selects feature points and feature for identifying a person through statistical learning. The apparatus includes input means for inputting a face image detected by arbitrary face detection means, face parts detection means for detecting the positions of face parts in several locations from the input face image, face pose estimation means for estimating face pose based on the detected positions of face parts, feature point position correcting means for correcting the position of each feature point used for identifying the person based on the result of estimation of face pose by the face pose estimation means, and face identifying means for identifying the person by calculating a feature of the input face image at each feature point after position correction is performed by the feature point position correcting means and checking the feature against a feature of a registered face.
    • 面部图像处理装置通过统计学习选择特征点和特征来识别人。 该装置包括用于输入由任意面部检测装置检测到的面部图像的输入装置,用于从输入面部图像检测几个位置中的面部部分的位置的面部部位检测装置,基于检测位置估计面部姿势的面部姿势估计装置 特征点位置校正装置,用于根据面部姿势估计装置的面部姿势的估计结果校正用于识别人的每个特征点的位置,以及面部识别装置,用于通过计算特征来识别人物 通过特征点位置校正装置执行位置校正之后的每个特征点处的输入面部图像,并针对登记面部的特征检查特征。